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<h1 id="article-shares-prediction">Article Shares Prediction</h1>
<p>Shyam Gadhwala & Kamlesh Pandey</p>
<h1 id="library">Library</h1>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(readr)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyr)</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(caret)</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(glmnet)</span></code></pre></div>
<h1 id="introdcution">Introdcution</h1>
<p>We are living in a digital world where people are more concerned
about the digital footprint and people often consider the like and
shares they get on a post that they publish online as an important
metric. We have several social media websites and print media to share
our articles. Online print media platform like <a href="www.mashable.com">Mashable</a> publishes thousand of online media
everyday and it is important for them get a more user engagement from
the the article they post online. In this data set we are trying to
predict the shares using that data that would have several potential
influencing factors, in other words, predictors, such as text sentiment
polarity, rate of negative words, rate of positive words, to name
some.</p>
<p>Why this analysis is important ? From this predictive model, Mashable
can potentially predict the number of shares they can receive based on
the type of article they are publishing online.</p>
<h1 id="data">Data</h1>
<p>The data set summarizes a heterogeneous set of features about
articles published by in a period of two years.The data set has 39644
entries and 61 feature columns. The project is aimed to subset the
original data set based on type of data channel (one of the six type)
and then to predict the number of shares.</p>
<p>For this part of the project we are using <strong>TECH</strong> data
channel for training and building a predictive modeling and extending
same models to other five data channels.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>newspopData <span class="ot"><-</span> <span class="fu">read_csv</span>(<span class="st">'OnlineNewsPopularity.csv'</span>)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="co"># select specified data channel from params and drop other data channel columns</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>newspopData <span class="ot"><-</span> newspopData <span class="sc">%>%</span> <span class="fu">filter</span>(newspopData[params<span class="sc">$</span>data_channel] <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%>%</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>( <span class="sc">-</span><span class="fu">starts_with</span>(<span class="st">'data_channel_is_'</span>), <span class="sc">-</span>url)</span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>newspopData</span></code></pre></div>
<pre><code>## # A tibble: 7,346 × 54
## timedelta n_tokens…¹ n_tok…² n_uni…³ n_non…⁴
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 731 13 1072 0.416 1.00
## 2 731 10 370 0.560 1.00
## 3 731 12 989 0.434 1.00
## 4 731 11 97 0.670 1.00
## 5 731 8 1207 0.411 1.00
## 6 731 13 1248 0.391 1.00
## 7 731 11 1154 0.427 1.00
## 8 731 8 266 0.573 1.00
## 9 731 8 331 0.563 1.00
## 10 731 12 1225 0.385 1.00
## # … with 7,336 more rows, 49 more variables:
## # n_non_stop_unique_tokens <dbl>,
## # num_hrefs <dbl>, num_self_hrefs <dbl>,
## # num_imgs <dbl>, num_videos <dbl>,
## # average_token_length <dbl>,
## # num_keywords <dbl>, kw_min_min <dbl>,
## # kw_max_min <dbl>, kw_avg_min <dbl>, …</code></pre>
<h1 id="exploratory-data-analysis">Exploratory Data Analysis</h1>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(newspopData<span class="sc">$</span>shares)</span></code></pre></div>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu.
## 36 1100 1700 3072 3000
## Max.
## 663600</code></pre>
<p>Dividing the popularity of the shares based on quantiles to classify
them as to how popular they are. The 1st quarter percentile would be
popularity index 4, next quarter be index 3, so on and so forth. Higher
the number of shares of that article, higher the popularity index.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>pShares <span class="ot"><-</span> newspopData<span class="sc">$</span>shares</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>pShares <span class="ot"><-</span> <span class="fu">sort</span>(pShares)</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>quantileValues <span class="ot"><-</span> <span class="fu">quantile</span>(pShares, <span class="fu">c</span>(.<span class="dv">25</span>, .<span class="dv">5</span>, .<span class="dv">75</span>)) </span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>newspopData <span class="ot"><-</span> newspopData <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="at">article_popularity_index =</span> <span class="fu">if_else</span>(shares<span class="sc"><</span>quantileValues[<span class="dv">1</span>], <span class="st">"4"</span>,</span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">if_else</span>(shares<span class="sc"><</span>quantileValues[<span class="dv">2</span>], <span class="st">"3"</span>,</span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">if_else</span>(shares<span class="sc"><</span>quantileValues[<span class="dv">3</span>],<span class="st">"2"</span>, <span class="st">"1"</span>))))</span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>newspopData<span class="sc">$</span>article_popularity_index <span class="ot"><-</span> <span class="fu">as_factor</span>(newspopData<span class="sc">$</span>article_popularity_index)</span></code></pre></div>
<p>Starting EDA with the visualization of the target variable shares as
a function of the most obvious factor, number of words in the
article:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>newspopData<span class="sc">$</span>scaledShare <span class="ot"><-</span> <span class="fu">scale</span>(newspopData<span class="sc">$</span>shares, <span class="at">center =</span> T, <span class="at">scale =</span> T)</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co">#calculating correlation index</span></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>corrIndex <span class="ot"><-</span> <span class="fu">round</span>(<span class="fu">cor</span>(newspopData<span class="sc">$</span>scaledShare, newspopData<span class="sc">$</span>n_tokens_content),<span class="dv">2</span>)</span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a><span class="co"># plotting the shares as a function of words in an article</span></span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(newspopData, <span class="fu">aes</span>(<span class="at">x=</span> n_tokens_content, <span class="at">y =</span> scaledShare)) <span class="sc">+</span></span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>()<span class="sc">+</span></span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">subtitle =</span> <span class="st">'Word Count v/s Number of Shares'</span>,</span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">'Number of Share (Scaled)'</span>,</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">'Number of Words in article'</span>, </span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="fu">toupper</span>(<span class="fu">str_replace_all</span>(params<span class="sc">$</span>data_channel, <span class="st">"_"</span>, <span class="st">" "</span>)),</span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a> <span class="at">caption =</span> <span class="st">'Source: News Popularity Dataset'</span>) <span class="sc">+</span> </span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_text</span>(<span class="at">x =</span> <span class="fu">max</span>(newspopData<span class="sc">$</span>n_tokens_content)<span class="sc">/</span><span class="dv">2</span>, </span>
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="fu">max</span>(newspopData<span class="sc">$</span>scaledShare)<span class="sc">/</span><span class="dv">2</span>, <span class="at">size =</span> <span class="dv">4</span>, </span>
<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a> <span class="at">label =</span> <span class="fu">paste0</span>(<span class="st">"Correlation coefficient = "</span>, corrIndex), <span class="at">color =</span> <span class="st">'red'</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>We can inspect trend of Number of shares as a function of Number of
words in the article. If the points show an upward trend, then the
article with high number of words are shared more. However, if we see a
negative trend then we can estimate that with increasing number of words
in the article, number of shares decreases. This trend can be
investigated further with the correlation coefficientm which in this
case is 0.07.</p>
<p>Now, creating a new variable here that help eliminates the dummy
variable for each day of the week. This new variable will take the value
of each day of the week that corresponds to the dummy variable’s
value:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>newspopData <span class="ot"><-</span> newspopData <span class="sc">%>%</span> <span class="fu">select</span>(<span class="sc">-</span>scaledShare)</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>newspopData <span class="ot"><-</span> newspopData <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="at">day =</span> <span class="fu">if_else</span>(weekday_is_monday<span class="sc">==</span> <span class="dv">1</span>, <span class="st">"Monday"</span>, </span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">if_else</span>(weekday_is_tuesday<span class="sc">==</span> <span class="dv">1</span>, <span class="st">"Tuesday"</span>,</span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">if_else</span>(weekday_is_wednesday<span class="sc">==</span><span class="dv">1</span>, <span class="st">"Wednesday"</span>,</span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">if_else</span>(weekday_is_thursday<span class="sc">==</span> <span class="dv">1</span>, <span class="st">"Thursday"</span>,</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">if_else</span>(weekday_is_friday<span class="sc">==</span> <span class="dv">1</span>, <span class="st">"Friday"</span>,</span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">if_else</span>(weekday_is_saturday<span class="sc">==</span> <span class="dv">1</span>,<span class="st">"Saturday"</span>,</span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">if_else</span>(weekday_is_sunday<span class="sc">==</span> <span class="dv">1</span>, <span class="st">"Sunday"</span>, </span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a> <span class="st">"-"</span>))))))))</span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>newspopData<span class="sc">$</span>day <span class="ot"><-</span> <span class="fu">as_factor</span>(newspopData<span class="sc">$</span>day)</span></code></pre></div>
<p>Some statistics based on the variables is as shown:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(newspopData <span class="sc">%>%</span> <span class="fu">group_by</span>(day) <span class="sc">%>%</span> <span class="fu">summarize</span>(<span class="at">Count_of_Articles =</span> <span class="fu">n</span>()))</span></code></pre></div>
<table>
<thead>
<tr class="header">
<th align="left">day</th>
<th align="right">Count_of_Articles</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Monday</td>
<td align="right">1235</td>
</tr>
<tr class="even">
<td align="left">Tuesday</td>
<td align="right">1474</td>
</tr>
<tr class="odd">
<td align="left">Wednesday</td>
<td align="right">1417</td>
</tr>
<tr class="even">
<td align="left">Thursday</td>
<td align="right">1310</td>
</tr>
<tr class="odd">
<td align="left">Friday</td>
<td align="right">989</td>
</tr>
<tr class="even">
<td align="left">Saturday</td>
<td align="right">525</td>
</tr>
<tr class="odd">
<td align="left">Sunday</td>
<td align="right">396</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(newspopData <span class="sc">%>%</span> <span class="fu">group_by</span>(day, article_popularity_index) <span class="sc">%>%</span> <span class="fu">summarize</span>(<span class="at">Count_of_Articles =</span> <span class="fu">n</span>()))</span></code></pre></div>
<table>
<thead>
<tr class="header">
<th align="left">day</th>
<th align="left">article_popularity_index</th>
<th align="right">Count_of_Articles</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Monday</td>
<td align="left">4</td>
<td align="right">280</td>
</tr>
<tr class="even">
<td align="left">Monday</td>
<td align="left">1</td>
<td align="right">311</td>
</tr>
<tr class="odd">
<td align="left">Monday</td>
<td align="left">2</td>
<td align="right">300</td>
</tr>
<tr class="even">
<td align="left">Monday</td>
<td align="left">3</td>
<td align="right">344</td>
</tr>
<tr class="odd">
<td align="left">Tuesday</td>
<td align="left">4</td>
<td align="right">357</td>
</tr>
<tr class="even">
<td align="left">Tuesday</td>
<td align="left">1</td>
<td align="right">345</td>
</tr>
<tr class="odd">
<td align="left">Tuesday</td>
<td align="left">2</td>
<td align="right">358</td>
</tr>
<tr class="even">
<td align="left">Tuesday</td>
<td align="left">3</td>
<td align="right">414</td>
</tr>
<tr class="odd">
<td align="left">Wednesday</td>
<td align="left">4</td>
<td align="right">368</td>
</tr>
<tr class="even">
<td align="left">Wednesday</td>
<td align="left">1</td>
<td align="right">339</td>
</tr>
<tr class="odd">
<td align="left">Wednesday</td>
<td align="left">2</td>
<td align="right">326</td>
</tr>
<tr class="even">
<td align="left">Wednesday</td>
<td align="left">3</td>
<td align="right">384</td>
</tr>
<tr class="odd">
<td align="left">Thursday</td>
<td align="left">4</td>
<td align="right">329</td>
</tr>
<tr class="even">
<td align="left">Thursday</td>
<td align="left">1</td>
<td align="right">286</td>
</tr>
<tr class="odd">
<td align="left">Thursday</td>
<td align="left">2</td>
<td align="right">339</td>
</tr>
<tr class="even">
<td align="left">Thursday</td>
<td align="left">3</td>
<td align="right">356</td>
</tr>
<tr class="odd">
<td align="left">Friday</td>
<td align="left">4</td>
<td align="right">164</td>
</tr>
<tr class="even">
<td align="left">Friday</td>
<td align="left">1</td>
<td align="right">259</td>
</tr>
<tr class="odd">
<td align="left">Friday</td>
<td align="left">2</td>
<td align="right">272</td>
</tr>
<tr class="even">
<td align="left">Friday</td>
<td align="left">3</td>
<td align="right">294</td>
</tr>
<tr class="odd">
<td align="left">Saturday</td>
<td align="left">4</td>
<td align="right">24</td>
</tr>
<tr class="even">
<td align="left">Saturday</td>
<td align="left">1</td>
<td align="right">189</td>
</tr>
<tr class="odd">
<td align="left">Saturday</td>
<td align="left">2</td>
<td align="right">196</td>
</tr>
<tr class="even">
<td align="left">Saturday</td>
<td align="left">3</td>
<td align="right">116</td>
</tr>
<tr class="odd">
<td align="left">Sunday</td>
<td align="left">4</td>
<td align="right">26</td>
</tr>
<tr class="even">
<td align="left">Sunday</td>
<td align="left">1</td>
<td align="right">158</td>
</tr>
<tr class="odd">
<td align="left">Sunday</td>
<td align="left">2</td>
<td align="right">121</td>
</tr>
<tr class="even">
<td align="left">Sunday</td>
<td align="left">3</td>
<td align="right">91</td>
</tr>
</tbody>
</table>
<p>This table shows the shares of articles on each day of the week. For
tech based articles, we can expect a rise in shares in mid week or
Fridays or after any tech has been launched. For entertainment and
lifestyle articles, post-weekend period would be the most active period
of sharing. While world, social media and business would not have a
definitive trend.</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(newspopData, <span class="fu">aes</span>(<span class="at">x =</span> day, <span class="at">y =</span> shares<span class="sc">/</span><span class="dv">1000000</span>)) <span class="sc">+</span></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">'identity'</span>, <span class="at">width =</span> <span class="fl">0.5</span>, <span class="at">fill =</span> <span class="st">'tomato3'</span>)<span class="sc">+</span></span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">subtitle =</span> <span class="st">'Number of Shares (Million) Vs Day of Week'</span>,</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> <span class="at">caption =</span> <span class="st">'Source : News Popularity Dataset'</span>,</span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">'Total Share count in Million'</span>,</span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">'Day of the Week'</span>,</span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="fu">toupper</span>(<span class="fu">str_replace_all</span>(params<span class="sc">$</span>data_channel, <span class="st">"_"</span>, <span class="st">" "</span>)),) <span class="sc">+</span></span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">65</span>, <span class="at">vjust =</span> <span class="fl">0.6</span>)) <span class="sc">+</span> </span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">plot.caption =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">9</span>, <span class="at">color=</span><span class="st">"red"</span>, <span class="at">face=</span><span class="st">"italic"</span>, <span class="at">hjust =</span> <span class="dv">1</span>))</span></code></pre></div>
<p><img 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btqGmEnQlAZ2GBECzrzcTsIKsIkJmwwwgWtSjWNsBMhqAxsMNIFxSUfTQ55u7DBCBcUl3w0OuTtwgYjXFD0QRsd8nZhgxEuaPmSDwhKPOTtwgYjXNDwDCgEPZbYYIQLir/V2eiQtwsbjHBBq1JNI+xECCoDGwwEpe3F1WMJ1JZhgxEuKE7xjQ55u7DBCBfUafpK6Kvqq2mEnQhBZWCDaYWgaXLqOgStccjbhQ2mJYLiFF/rkLcLG0w7BB2ggtY65O3CBiNcUDdICv65pGoaYSdCUBnYYIQLWpVqGmEnQlAZ2GBaLGh1CDsRXx4mA9uWlAU1l310Q9tV607YiaigMrDBSK+gQz1+P9gKGVpNI+xECCoDG4xwQXFVZ6ND3i5sMBCUthdXjyVQW4YNRrigOMU3OuTtwgYjXVAMkpoc8nZhgxEv6PxU0wg7EYLKwAYDQWl7cfVYArVl2GCkCzrZ7s679riaRtiJEFQGNhjpgg420nlXx1fTCDsRgsrABiNcUEwzNTrk7cIGA0Fpe3H1WAK1ZdhghAuKedBGh7xd2GCkC5ommAetf8jbhQ1GvKDzU00j7EQIKgMbDASl7cXVYwnUlmGDgaC0vbh6LIHaMmwwEJS2F1ePJVBbhg0GgtL24uqxBGrLsMFAUNpeXD2WQG0ZNhjxgg47nf4wdFk8BD1u2GCkCzo49fFWH+/F1zzk7cIGI1zQg62+frcTb3XWO+TtwgYDQWl7cfVYArVl2GCEC5oO9Ske78XXPOTtwgYjXVC8F9/kkLcLG4x4QUvZP9/rXYSgxxQbjHBBy38n6fnbV9L9N65A0OOJDUa4oOW/NLd3Tv2zcxGCHk9sMMIFTWfm6HUVTdMXVOb3BHQIOxHfbicD25ZU/pWPw8sX/Lpq3Qk7ERVUBjYY6RV0Ks/fyvyEoMcNG0y7BN0/fzG/U00j7EQIKgMbjHRBx5uFU3zJTwh63LDBCBd0st2dbPf9WH7U08Eo/phigxEuqFZz0MUf8qp5yIGdl5iCDjfwYZGaxwbYeYn53UzKzuAnlqtphN8Wgh5rbDBRv91ugD/kVfPYADsvuCaJDUugAjs3EJQNS6ACOzc886AQlHhsgJ2XePOgwS9XhqDANgvPx+0gKPHYADsvPB9YhqDEYwPsvETrgwan6CEosA0TRVD/YVAMkuoeG2DnBdNMbFgCFdi5gaBsWAIV2LmJJOjwxCVzosd18fWODbDzEkfQRPmpZ0LxzSI1jw2w8xJFUP1BkXS82Q9c2wlBgW2USKP4vq2i+DxozWMD7LzEE9QUTwha79gAOy+RTvF992b8AKf4WscG2HmJM0hS1dN0QZNO6A3Pahrht4WgxxlLoB6BnRU0HegZpsl28AP1EBTYyFS6oJVZVruEYQlUYGnUyIJWh9KuVmGZdsKxxRKoC2JRQWNRgaVRcYqPgSVQgaVRIWgMLIEKLI0KQWNgCVRgaVSqoPjAckMqsDQqKmgMLIEKLI0KQWNgCVRgadQ6guKLGxpQgaVRawha/gJbCEqjAkuj1hAUX2DbhAosjVpTUHyBbV0qsDRqnT4ovsC2ARVYGrWOoPgC2wZUYGlUTDPFwBKowNKoNfugKa5JqkkFlkaFoDGwBCqwNCpZ0GH2Xnzoa2yX1S5hWAIVWBq1fgUNZlntEoYlUIGlUTFIioElUIGlUWsJak7z+PKwWlRgadQ6gg718AhfHlaPCiyNilF8DCyBCiyNCkFjYAlUYGlUnOJjYAlUYGlUDJJiYAlUYGlUTDPFwBKowNKoVEFDs/T7b16DoMDyUCMIutc7C0GBFSvozpnvooICK1fQ7BT/gsrMulIo7aomCcMSqMDSqNRvtwt9swj6oAQqsDQqBkkxsAQqsDQqBI2BJVCBpVEhaAwsgQosjQpBY2AJVGBpVLyTFANLoAJLo0LQGFgCFVgaFYLGwBKowNKoEDQGlkAFlkaFoDGwBCqwNCoEjYElUIGlUSFoDCyBCiyNCkFjYAlUYGlUCBoDS6ACS6NC0BhYAhVYGhWCxsASqMDSqBA0BpZABZZGhaAxsAQqsDQqBI2BJVCBpVEhaAwsgQosjQpBY2AJVGBpVAgaA0ugAkujQtAYWAIVWBoVgsbAEqjA0qgQNAaWQAWWRoWgMbAEKrA0KgSNgSVQgaVRIWgMLIEKLI0aWdDqUNrVKizTTji2WAKV+uVhR2RZLxxhWAIVWBoVp/gYWAIVWBoVgsbAEqjA0qgQNAaWQAWWRoWgMbAEKrA0KgSNgSVQgaVRIWgMLIEKLI0KQWNgCVRgaVQIGgNLoAJLo0LQGFgCFVgaFYLGwBKowNKoEDQGlkAFlkaFoDGwBCqwNCoEjYElUIGlUSFoDCyBCiyNCkFjYAlUYGlUCBoDS6ACS6NC0BhYAhVYGhWCxsASqMDSqBA0BpZABZZGhaAxsAQqsDQqBI2BJVCBpVEhaAwsgQosjQpBY2AJVGBpVAgaA0ugAkujQtAYWAIVWBq1uaDP3+q9fAOCAstDbSzo4eWL6egcBAWWh9pY0OffvJbuv3kNggLLQm0s6P7Xb6TP376ifnpBZe5mCMKZ+YLuvewF1aG8OLMXQZ0HAbs6qiTs0YLmFRSCisO2qrFMglL6oPHaBewKqZKwRwt6ePnCwqP4eO0CdoVUSdijBaXMg8ZrF7ArpErCLiBoKctqF7ArpErCQtBWY1vVWAi6fthWNRaCrh+2VY2FoOuHbVVjIej6YVvVWAi6fthWNRaCrh+2VY1diqB1wvQRKGBb1thGWAjaSmyrGgtB1w/bqsaKFRRBGgeCBjO6uOoWIDYQNJifvgVDZQSChvMchsoIBJ0TGCojEDSQvV6vd/ZfYKiEQNBAvp+a6wl6MHT1gaDzk18yGCWYGagTCDoTXTvPajVHZ64cuTEh7ZoZkPJyarOgTPtwdCH96E97vXNHb0lMq8ZdUl5OSxGUySSefXj4/pXn37y2d4EB3SpDhTR2KYJyvRp59uHOub1z6f6fR6a2b2Yg/t6tU6iWc4rnejVG5+6oE/vhf/3kXLoTu8HtmhngeTnVKVRL6oMyGMqxDw8/2dFfVnF4maEHahN5ZoArTC+nGhosQVCmkxvTPtxxQ/i44ZoZYB5rR3850Q1dgqC8J7fYk5UX9CsqrkUp38wAT++e5+VUr1Atb5oprklMJUkN4dW/e38YeYjENzPA0rvneTnVK1TsgjKZxFWS9Cgp3f9GfCzHzIAOg6GME20puVCxC8pjEts+VOOji/vnox5zvpkBrqkrjpdT3ULFLSiXSTwl6fDy2Wv75+P2QBlnBjh691wvp7qFir2CcpikdiJPSVJ7MbafOjwzA1li9u65Xk61CxWroEyvRrsTGUrS4XeuqS7dXmws08wA19QVz8upbqHiFJTx5MY1W/mtty6Yvx4RM0wzA1zjRIaXU5NCxXyKZzq5MdUkZegF8xf24oZnZoCpd8/wcmpUqHgFZRKJqybpjHrRB3QMMwM6TFNXLC+n+oWKVVA2kVh2YtanixuOmQHGqavRma/Ffzk1KFS8FZRp2punJqm9uHNxJ3p3mWNmgLN3f5GhuQ0KFa+gLCLpksQxG/SR7tM9/5O4IySumQGu3v0eC7VBoeKeB2UQiWmyMh2dG8UfwjPNDLANE3UnPL6iDQoVp6A8nTquksQ1lmGZGWDr3Y9Uz2Gv8AfcYqV2SeEUlOdVzlSSdPbPx389jRSSYWaAq3f/M44C2qRSsQpqykb0iRCeyUqmMXz8YbYNU7nXGUWvKU0qFaOgPP1tE4aSxNSpY9kJuirz9MNN9r71t3GBTSoV6yCJ4XTBVei4yj3PoIPrxW97n7H7940ayzyKj97fZpqs5DriI3PAow86VAmNfyJOf5aalxPDB0wbvEi5BNWVjuPSSI7JSus7S6VjGXO4jm1c71Wn1n6udC/2xWPNXqRcguou3eHl+J94Z5isdG/LcFQ6HYZSx1Pu95iua2z2IuUS9CN9UBjmgnhGr6yfKI485jA7laMw/9Mn6rT3RyyXiTZ4kbJVUJ55OnW6YJis5HpbhmXMsfONGzk7dg4v83xjRf0XKYug+29eO7x85grDPB1Ln47x03vxxxz6+o7nfxaTyJumL1IWQdUZU+vJM0/H0KfjeltGJ/aYQ5+aRpzffRI9zV6kPKf40TmuHnfKMI/M9y58L/4bvW9fcRU/chguIBl5B5q8SJn6oKavGP3tHp55ZB2ej0cxdG31QefYAQwXkOzpgjxqWqjYBklKpajTlftv6APNMI/MdG4b9c7xvDnF8MLXiX8ByY5+fZ65omt+g7C9k7QXeZC5445K/HlkpvemVE+caepKvaQYPmwY+wIS/Xke029qBo0vqHkPSR2auIbyfa8mx3tTttzzvDmlc3g56s615+HYF5CMzv6jrvUNe8zxBTWjbGNpzFLHMHb3ZIYP0vtyzzRbGTlxOouzsV8oMWq2BzhO8QwjeNWRYZkX2LvAMYRvydco+8TpLAajClXDwsLTB1Ud+fizfxzDg5+cP8fwQXq+cs+RSJ3FYP73a02PGNcgaf981I/afP8Tc/v8rdhnzMi9OROucs+VOJ1FprT5D3nFyU78t/aZyj1bonQWmbLWgv6f/dhF7K8pYCv3jGneWWTKWgu609N5+ac8F5+1Ks07i0xZa0Hth8vUybhFpW7dstaCqhoKNYVnzQVl+eQaEjFrLKi9uD72EAmJm/UV9PCfzfuxZ76LIZLkrK+gLoeXz/zFqtuAzM/aC4rIDgRFRAeCIqIDQRHRgaCI6EBQRHQgKCI6EBQRHQiKiA4ERUQHgiKiA0ER0YGgiOhAUER0IOjCmWx3dDaO2GzYOXFJ3z79IB2/eGlm9eDUdbPRhiHOwEIPWetA0IUz2e6af41hc3Ow1Te32rSQbcnJqxryqqYcbHWnV0PQqUDQhWMFzQycE2/YPEHHm+rhB6+894palZyYWQ9BpwJBF44TNB1uaM3Uyb6bDjbc/dT2ADbMCn1f33bHL76jNuvbdbpyekpy6sf6ZqjKqF/lbrWgyZHdiDUKBF04XtDk1HVTRYcnr+rz9WS7b9dumP9LFXRTGag2M33Noesa6NtB12itbvwqf6seZmos4gJBF04m6MmrX2jXlEta1PHpq26p+acsaN/8YNb5rkGihTUP00v8Kn87fvHdzZmO6ToHgi6cQgVV/6hzt+pBqjrozvBmqa5/031Q9c/QDP879uEHr1zSTmsZ1Y1f5W911wAFtBAIunC8oKrjebB1wuo3Pv3htiuMVYKWBv6DrnHa3hRP/OZhm/1B9TTBmgWCLpzCKN4WUSXpZPs1e4a3I/LpU7wTtDxaH24MutmNX+VvTb8B5/g8EHThFOZBtU7jTe3U0I+4pwdJpnvqBM0eYzJ+yWxib/wqf6sfMpydfVrfQNCF495JMuVtoHqg7+lRTz7kttNM+UTmoLPhBTXrMusOtsw53M34+1Xu1m6Nk3wWCNosbgyPcAWCNssQ/UXeQNAmMTPxCGcgKCI6EBQRHQiKiA4ERUQHgiKiA0ER0YGgiOhAUER0IOg65Ont3d27zxbf/v49/e+D+Y+4v6vyWXjd5M6z8r1JccNH6oE3H+erw5B8MQRdg0xuf55OHt1bfPs72rGDWxUbPNPUJ8GV4+knSooL7qsHPd19ElwXeggEXYMY1Q5UCf25KXv6rvo/+ezBvckDU87U8odmy8TcjO89emhFMyvGt9IDpdSjxwe3d28+sRvof5VrFph8dnv3Xs5Vj33glj+4N743Vj9rlx888U3JNxlrvt3Ywe0z+vZA0PXI/buf65tH955N7j82eqn/H918lt43+unlxXKYPFQL0+SxW6Glu/NEVU219a9u2Q30P7c88P6tElc97rNUL9DPoChKZPWflTr7122iVvifDNw15X7WGgi6Fklu3/k8PVC+6JOnMsh481jLqWKXP8y3fvR4clu741YoQT9NHo8fpsnNX7oNdBf07jMHNCd89XjP1XJ98cV98wzqnl6tVpkCarZxZ3C9ie2vmo0N3D1joRsLQdckv9p9YrRQ/2iDrDe2q5do3XYLgj54ZrVzKyZ3Dx4mjx+pR33xQAuUDYMc8MDcPCxwH+ze+XTX/KT+12d1VZNtr9KWxkcP3SZ6nf3Jwt0zFrq/EPT4x9bJRw+NT/cfm67izWfaGFs2pwcqdoP/uedXTO78/Enyi3taK1sBvT8OOJ7m6uXuGdRCe+a3BdSqrdblm7ifLNw9Y2GcBUGPfya3f2lGzmPdJTTCTB7cM3KOVefx9mN9Xk3u5tsbPR7deZj6FQ/uqk6oOgnbrmbujwOq5emjIlc9ZnLfjpbG7ol2C+Y9Vf2HfBP/k4G7Zyz0NyDoGuTpnd3dm5+bc7kayk9u7979ue0xqs6kHsQnfpbUVVT973g3X6HGL8pDM/DONzA/mPX3f3F7ivtg9+ant3yfNNm1qrsHqId8nvpNEjOI1z85uCUmeYcDgiINM28+tJjiEIwWCIo0TMWEvt9i9+5Rm8wNBEVEB4IiogNBEdGBoIjoQFBEdCAoIjoQFBGd/weoTB1PcBgx1wAAAABJRU5ErkJggg==" /><!-- -->
From this bar chart we can visualize the shares trend across the week.
The users engagement with the type of data Chanel (tech, entertainment,
politics) will be different across the week. Users may be more inclined
toward sharing lifestyle and entertainment news during weekend and
prefer less to share technology/science related news during same
time.</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(newspopData, <span class="fu">aes</span>(<span class="at">x =</span> global_rate_positive_words, <span class="at">y =</span> global_sentiment_polarity)) <span class="sc">+</span></span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">col =</span> day, <span class="at">shape =</span> article_popularity_index)) <span class="sc">+</span> </span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_smooth</span>(<span class="fu">aes</span>(<span class="at">col =</span> day), <span class="at">method =</span> <span class="st">'lm'</span>, <span class="at">se =</span> F) <span class="sc">+</span> </span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_shape_discrete</span>(<span class="at">name=</span><span class="st">"Article Popularity Index"</span>, <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">"4"</span>, <span class="st">"3"</span>, <span class="st">"2"</span>, <span class="st">"1"</span>))<span class="sc">+</span></span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">subtitle =</span> <span class="st">'Positive Rate VS Sentiment Polarity Plot'</span>,</span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">'Global Positive Word Rate'</span>,</span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">'Global Sentiment Polarity'</span>,</span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="fu">toupper</span>(<span class="fu">str_replace_all</span>(params<span class="sc">$</span>data_channel, <span class="st">"_"</span>, <span class="st">" "</span>)),</span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a> <span class="at">caption =</span> <span class="st">"Source : News Popularity Dataset"</span>,</span>
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a> <span class="at">color =</span> <span class="st">'Days'</span>,</span>
<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a> <span class="at">size =</span> <span class="st">'Shares per Million'</span>) <span class="sc">+</span> </span>
<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">plot.caption =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">9</span>, <span class="at">color=</span><span class="st">"red"</span>, <span class="at">face=</span><span class="st">"italic"</span>, <span class="at">hjust =</span> <span class="dv">1</span>))</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(newspopData, <span class="fu">aes</span>(<span class="at">x =</span> global_rate_negative_words, <span class="at">y =</span> global_sentiment_polarity)) <span class="sc">+</span> </span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="fu">aes</span> (<span class="at">col =</span> day, <span class="at">shape =</span> article_popularity_index)) <span class="sc">+</span> </span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_smooth</span>(<span class="fu">aes</span>(<span class="at">col =</span> day), <span class="at">method =</span> <span class="st">'lm'</span>, <span class="at">se =</span> F) <span class="sc">+</span> </span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_shape_discrete</span>(<span class="at">name=</span><span class="st">"Article Popularity Index"</span>, <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">"4"</span>, <span class="st">"3"</span>, <span class="st">"2"</span>, <span class="st">"1"</span>))<span class="sc">+</span></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">subtitle =</span> <span class="st">'Positive Rate VS Sentiment Polarity Plot'</span>,</span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">'Global Negative Word Rate'</span>,</span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">'Global Sentiment Polarity'</span>,</span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="fu">toupper</span>(<span class="fu">str_replace_all</span>(params<span class="sc">$</span>data_channel, <span class="st">"_"</span>, <span class="st">" "</span>)),</span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a> <span class="at">caption =</span> <span class="st">"Source : News Popularity Dataset"</span>,</span>
<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a> <span class="at">color =</span> <span class="st">'Days'</span>,</span>
<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a> <span class="at">size =</span> <span class="st">'Shares per Million'</span>) <span class="sc">+</span> </span>
<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">plot.caption =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">9</span>, <span class="at">color=</span><span class="st">"red"</span>, <span class="at">face=</span><span class="st">"italic"</span>, <span class="at">hjust =</span> <span class="dv">1</span>)) </span></code></pre></div>
<p><img 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SrZ4T32y324tEyg0l+SAVC5WH+16I334dIygUp/SS4F0LSRwVaA1idoWQJjZDxglf6SXAqgNe8xz3P42AZo2sx3yxIYI+MBq/SX5LIBxYX5Vy0s6CAlMEbGA1bpL8kAaAB0Jir9JbkUQP1dnQLongrggGmZQKW/JBcEaO7pi3f4rAuTdiiAde+cA2U8YJX+klw4oI4EQCdV6S/JAKiRAOikKv0l2QwoDJjfwOacm1tWnqa5nIPP8xwe0LTufe0I6Fqkc87eKofMOblKf0kaJpMkqQB6flLQUPlze7j8AQIKzlBEHpHfX09EdiuAYEH7Z/SXJIJZFrNP0tceXf3gVfjngcw5psnG1rbHe56IvAXQp6/ceeEdPn722r06QEF4fcW6gFL9aLwA6Cgq+wCK0zV//ic4G4nmHMtED7Pt8Z4nIjcDCky+/SJ/eftOC0DrQp4N40UDoKOorAPU1PBWHc9XYf/YrytbqP6ROcc0VW68icjNgD79/lvFk++8hcdP/v1/aAtoSmdsIAXQ6voiUQB0FJX+Em5ugxbnp+enyhCqJijPOebJxuNNRLYBvbxbHrf/5LvvFE+/9zocPvuL/0ZV/FeUlO8imyS4q9Qm9jGI+kRA3bz9Hz7IzrIlzLS5pazgxdd/goYSTpQs6P4nIrsW9Ky0lNnnL2hA3365XRvUEqdK5xp+NwvaaCLGyHjAKnsBevn7ys+5+sFXHxY859iebHx7jInI5SoejLYB31hQddQMaGmTGZ/b7l+kMQA6jspegF7dhwDo2S0iAypYmmxsLSe254nI1TYoIHqTb23aoG/fAXm5FtDKNkiKw/KKjBVAdcug8z60vhIYI+MBq+wF6C4yzETkMqCKfogXcEX/7LWXjRffYEHzPIqyKHJ63itue8V6EprBgo6j0l/++wN0oInIDqCwoi5GCTbahGIclIxoA6BZjIDaA5oqgFar9wDomCr95b9HCzqMuF78zZathsq7iGMFqH1CwZolMfYtcQ1eAlQdBkDHVOkvyQMD1DWf9VJ5Fwq3zGlxAqAZAiptzJL9jInQ4CSNpdJfkgsBVK/Nq89UAbUJVbW/+TZ50S1Dpb8kDwjQ8yORWw3pSSrvojKingBN/IBi+zQAOq5Kf0keEKDagraQyruA0Xa5dEQikiakJFEk/hoHQCdR6S/JgwK0vVTeRZYrQO3pR2g2q3HQlQ2ocvt3KoADpmUClf6SPBxAlfnErv8jE6avl8q7yMrz43yAogClFIKCsBTY1qL3yngtii7tm7G/yoEzBkB7SPVl+ABV1Xx5/JEACluCYDM12i+gNaNTJ6dlApX+kjwoQNsP16+8C0VYnthekh9QbpcGQCdQ6S/JgwJ0ByepAigMZQIWEdBS+BN66RWREpjaI6B1w6cnp2UClf6SPChAi/P6bUNdKb+KCAG1Bn2kaUKefBnQVRTzrl+RBKCsUU9p23dPqtoB6rnp5LRMoNJfkgcF6A5OEgIKB0xEmiJ9PA/TBTSTCFQF0NYLiHHOw6VlApX+kmwEVHY7/qumHWX3vKHscLM6yU0iQIW+TLVBIWBvKv88iiFFnGYaM9OH7wXUt35OALR7Rn9JGiZVmXksqN7Iq1YOAlCFIrRBFTQIaGIBqgeBKiJjGf5JgEpmOaC85QZpAHSYjP6SRDDLUgX0X8EwNx6lfPHVb958ONqOxw6goKdXFR9FUMUnAiiOHYlw+N1afVUWlNwhrNcztKDxToC2bYO2K7pRck6u0l+S7QClScYC6PHpiDseu2GmE57ztE0q7yJLEvWfXok2jmXwyDoBHB1AFaHO2JLIquQ9yzs0LJF3uLRMoNJfknYNb9Xx+rq9V6f1Md6Ox+UwE0+N2iKVd8FcaUA1feqsC2gMgOrReaUpH4h5AHQvKv0l2boN6gI63o7HZUCV3e4zHpR613XcUQwobkAngNIAEkQVAY05/qT4zPVtOu0Idri0TKDSX5Lbwkx1gBZj7XjstEHPkM4W0dDKuygBmtmAQvVvLCgcrVcCKGQzgFLi0YtulJyTq/SXZBdAedNj+DbejselOUknyli3mOxUfhVoKAFD1bwERkuArlZrGhEasWldE6AQrwc+o1y3QjvtSHu4tEyg0l+SXQDlTY+pXh9rx+NhwkwxVOQJDqdDwqDq1m5QBksnOmNE12sJ1qMFVe1MlSiKKsPyRyq6UXJOrtJfktsA7ST72PF4oNFMUSpkZbFpgOIpGMmsJAMDGxGeStDzIddKgvxoSTN35tIoRTdKzslV+ktySED3suOxNR5UT/no7iRxpQ7tz8xqgMYJ1+gIqEoTa0DXZUAzrOrzAOi+VPpLclALug8ZxoImBGiCDVDdAqUqPM+IyCSNIh20V5dydq0yBhQXfiBA03GLbpSck6v0l+RCAI0FOy+gqvZWrhAAqpqqeU6tzzyWxR0E0EwBmkCm1mNGDpeWCVT6S/LAAMWZnS1iWZV3EXGDUwAFy8heELpHCbr5CY/BU5dyBBXOcBWfIqkYNkVA4xZ1/eHSMoFKf0keFqDn1P+/ndDyq8A4PLVCpTudAY2xDo+4iykmXx4nzAOhK2YXgwAJ8hkA3ZNKf0keFKDtF24oi16ljiwo8gl1fJYV4COh2VRVfGwBmgGgBVlXahkUvMwt2WAEdKAfGaRWrimg5b9VwC7KqU+duysZUGhZkvFEQGMymTRLOefhd4m0W1VFr6wtGGIGdBzbMkrOyVX6S/KgAO1fxa9glntMHZYRAopOvQNonNIoeowsgeuEQ5tkDQdqtyKgqsVAgKYB0AFV+kvysADt7SRlBCi3QWGqJllEapvagMYpAUoReQYUgZaYaJRjz6kLqL936XBpmUClvyQPDNC2UnkXCFZsvHgWq2ka03JhBKi+rhKTa8SAKmzVfXgIiTUJj7tMXek5F7RSdKPknFylvyQXAiiFijJ2wWGoZy4QxjQJCQBNcSNDjI3iereRqtJTGorPgArQ+lAioiVA8bD3bOUZ0DKBSn9JHhKgZ206OUnKr0IBinPdNaBcvROgcQ5TOQVQ7nhHQHHuJwPKLVeo4g2rAqALaBYA7Z7RX5IHBCisS3/ektDKu8hhMQZdxccrPdYuiS2rqAhcQ3Wv/CYCNAFEVR0fJetkvaZof0R9oBagzignGfMcAO2Y0V+SzYDSJDV0SniY8p7ncFZFA4qa26ovv4qIAMXG5jqFAU3slgOgMbRPEb1kna3jNHKQVX57muZrADQni0swWklW1gjo2AAa2qCdMvpL0jCp3nYV0Mp84+kAxSAojFhuI+VXgd3uNN1ovVbtzZU2oAn2aioruYbu+MgAKu57usZBIurMmnrpqfGa11TxsYzmG7DoRsk5uUp/SSKYZSkDChON36BdkfY8ybgqwwCaGNzW+VqsHQMKdboykgntVaOr/CjlfqMYht6taQJ9ngiguQYUuuhtQE38/nBpmUClvyTbAXp8qveV2+8k46oMAyiENBNshcJAz1WcZjJkhAcjA6Dl2BN21IN/jykoCqUAZkAzAZQCBMqd123QlcylP1xaJlDpL0m7hrfqeL6KbdBbek7S/icZV2UgCwohd+ARByIrRyjDMSNsCxHQFDs2YWqn4ZOG5zHE9soBFASA9UoY0ITiTeiDyTzk4CR1yugvyVZtUHs6534nGVdlmBH1gFQCHswK8RFAI+CMAaWxShmP/HQAVQmhIwolivXUJOiEigKgk3rxDqAjTDKuykADliMBlIKcRF5kAFUnqMMokbo/EUCp8brWizhF0YooVo5QBi1P4JP7lQKg/TP6S7ILoCNMMq7KwKvbxdLkJFGAytDPnMY4JUZgehLxRn316isMMElXOqqUIKCm8W4BaoWg+hRdz91rFw7o/icZV2UwQMGtUSCaEcsxOe4xV+bs1FO7NEGfKElzw7MASmHPVU6A2k6VA2i6kwWN+u6vvDBAG2Ufk4yrMtDyixAJSgShmA0kej8xdLzDIOUssgHliUlkcplPlQjXXqbBojDPLrPGjVQA3aHoAqAi/QHdyyTjqgw1WIRrb1yJMbUAxeHJSSSWUBjFFmgS53GsWwQ62JREeEiAppSFAIVmKTRWZYWdVkUniS0h2r1Ss0xZTaF3kGsH6EgyzIh6F1CSKMZmJgKaVQCVvick1DKMqdT83E6VJsNqtU5XBOiaTqbYsiD9pZF4DpN1gHoJzesW0vMXegcJgPaTYQDNCVBYAtwACpONKLaU6BalqeWJXQjlJ9SPT74P1PV4GLEFZUIVmDTOFOfYi1bhKUudx9kGaH2ZB0DnC+gum8mayGWi8eS1lowFxe6mRCZ4GCO5tgDFxmgusSoOlGJXaRrzWGjN5wrvQkep8zi9ATXItyv0DhIA7ScD7ZMUI4YQsScLGmlAnXFIscQ/rXBThIByw5Pnc1I35zqS6coRJEpLVTn1U9Fh6jzNboA2EhoAHVkGmvKRyMqeVG+n6LkzlxgBjWBYExwTqLmxoBlPSs400My3MaBptE7xS5ZyYEnzD3CmaQlCH5PDlHkAdGwZZhMFhBLaihydpDWWmLdcvHoAlHdJSg2gEfbfJykt8gDD7RKwnXYNn0ZJrAFFQnE5Mt0kXWeps+ViANST0V+SBwXo1f3trU+SyrvgzTskSm8GFmdQTZOljHPpVaJ1G6gDM4lyq2sJffsE/CPpCo3itcwBxUFS6NCvIiKUNFYA3V+ZB0DHlmHaoAyjZjQT2iI6icuA6/5QGL+cYWQK6vMIZysn2hxqQOVuMN6e6nyyyGA+aQ+7IgDaPqO/JA8K0P67HQNrMgyZzagDqAUnAkoVfESAYiwpgXEltG9iwpyCb6/gQwtKt87lPusIhpe4gHqnztd0hk5OywQq/SV5UIC2X5Wp8i40oLAZp9T0mV61gTvdYx7AHOGncpwSDWgEHjwBKivcQvsU5oesczKgETUMct3zaQClFumq0vqs666fnJYJVPpL8qAA7b+ZbEKL2bHzDdOSOGjJsXnoqwdAI45xIqCZjjUxwVTLZ7x3DUCep3RHB1BlQCneuios9z0AuiWjvyQPClCPPH3lzgvv4NGTb9+5c09OV96FuDhs2gRRXOkOwkZ5Ytai15LosD6RJ4AytOs0kYnza13FJ9TJRBOaYqzCKdhEMacAaG1GfwFvARRslrFXU0zybAb02Wv3irdfhKOn33u9ePLHr/P58qvQa4AJotE6yi1GEwKUaEs0rNQrT8aRCcXV7A2geFup0mF90dgClAJTPLSZPgRQmVcX2qDmm1cMk+rFVQBFt9nsmzU9oOdHR6f2Pl5Pv/9W8eQ7b6mjzwHTN8WEVt4FtS7FM4p0lWzHnmhHpDzOeNgyevbUAsi40z0D48vrMStA1xpQNrQQnc/XCCj6ULJLg3aQyoAOXebXENC0Ku6AZZzFCTONlTnF2cey4/Eos5Ddneae+5u7p3Y09Ml330HbSUJHX1FSvgu3Kin2bjOqY09MKNrJhFcOhSC+tr0EKPo7EbVBBVBjYvF+eSZNiNgQWn6geNi3dH2lGdCr+2SteKaxtR3i8ekYWx0XlTgo2HTLl//8BQvQZ6+9LOcrf6wcxEwMogIpIIX0wfBPaWly7zzSiuaSGwQRO/DcQQTh0ERRKyECMLWmvk9j3RsAj+BEQmssqNjayc3ZBCr95W/X8FYdr69vjmTnwctv2IDCMg6jzEJuBtS2oE9f0XxWA/W603FNZjS2GIWpIKkdbYq4owlmFycuoNI2gP6mdYwWtASo8pTYwqargiwoPsLaACrT71bl8cca5slpmUClv/yb26AoODkOZxq7gI4zC9ldYRmqeHuFZdMGVV78PZOw/CrYJBpGS3W9TFYiRglQ49XDHl9lQCUaCsNADaDSEUWARuo5xAfCUaLpypq/h6cDoOabV5q9+A229s5kprHXgu55FrLrJG1KKyxDrU5evMOnt4rPcAf4tZFSc9QM/UglPErefUwzlnnAHUuMFlSG21nrOaUUncKTEqVivSmPsm8DaJiTRNIMKHrxqpHJM43dHY/HmYXcKg6qjOjbd0DqvHgOyUPpJ4nFaO6EnoBA49bzYvUJ1fWKUidGmnEVH6+xo0iqeAiGUhOUoM9gd6UV8ZmXAAVy88pzYugJdhdZ9Voeb1mAYhwU2qA00/jq/s2H1o7Ho8xCHmjhhpydcVlHzFTRud0cjaCtGbNNpAi9lgqg6hZRBjOU1noOKLYRxKDGOhc+A06zo/YFBxQcW6pFIl795i0vDNAOsqdZyMOMB+WZxmDBKDJERPkZVTW8XUNbuyMTWNijmbGLHiuvSDbvpo3ookRWcMxk9DM+QxlQ/JvxjI7XIVk86rQ/faXQx8h4IIDuaxbyQONByYLKaGVuRoKN036TxWjKbUjsZYLupFymLVFmE4eizWjijIc/oxGN8gqgOQXoqeVLrpiMnF7ZFTk2PDWgUQD0wPrid5uTJBwk0NQjGmPbt+fVl6Q9qv0fWGIs4sg9ILXWeeIkU7fIMzwD0Src6gsATR1AVysLUOoY4I1A9ZbKK1pDhx5QtUFXchQAPSBA+48HpYBmTkvHr9YaUJm1YflNBtE01i5TznsuoNskgHJDExNFtCxeRGuJZ7QPiIl30v1sQFcUrLcBjSI9F8kBA60AACAASURBVN78gADoAQHafzyo7opUxzD0KBN2sjwz+yZVGc3NKPw4s+aJYFqxkgRoxKuTEPrSQboN0FUFUExPP0A3JXoX+hgZA6BGejtJ7N+QBV1HuQZUB5bkjKnthVGrtsexTciptqA5rg2qpzBHNH0u4kgBAh3xSiEpP4oQVxrRpKOxitBCLkAV32m2SAB0ZHGr+LZ9AZV3AaYyp0Xk17YFjTWgZO/ACzLVvYmPmiElEQXpDaBxwjOWiFAM4qOLowFlIUtpDGJk6nTcnhbJrQAaB0APBtD+ThKF2xEQtI3shCOhDGikTartNwmFkJomLlcBjQ2fsiEIXXKdrohMpgE01YBmCCgeBkBdOShA+ztJcRJTUIgB5S25sOIXQLVPI/DmNqOZ3haZRymRN0Thq3I0P8NtwGRevfHoV3YN7wfUboPiwmbc6OhX6B0kANpP3DZoZcR0jVTeBVJHjT491lNhlFjT5KBvM4fFG+B6xPW9a0jNKHwZ6JnYogGNqoBS01UcH3wQDajctFx07GJtgdN1oQKgI8swk+Ysf9gMRgb7h5yZ6XFiRbnCRypLlb21fjjW++ium1Xx1lTH4zo5fFPxrySmtaIxTrmxoNAGdTo1vYDWjB8JgE4qAy0BDuiAK+9U47hesgWoNEsBUPKoCWPXb8pNP1PKSzTKZvKZLFAvFb4FKBEvgKa0Il6ariMEMJE1czxFFwC9/oBCLbpO4SBDC8r7buo+dN2UNNZVmPMxGmnueNUcWWaZV4DwAUqNCq7KMTIVwTCRFoCKBEBnDSgOp+9bxROguF+hACorhkVkTxNcfR6u4V6zEdvGXE96jxMcYseMUm++QZRIzAXQJKkBNNIDqjgCoPRXAU0K99ElSFpdddk0XYalZZScAw238+Iw2qayw1hQbf3gi+LPGlQf6QqfPqnTCAHlep59HMBVIkw46iPXMSrijTx7vHOGhNrsakDNxqEYjuVVnNbMJ7C2xiGr8ujGfWq2oPzzri+g6udVAC1NO7ZkEkD7LwFuLChIDBZ0bQFqVewxtiyzvAwoTudIZSy9Ee0yMaCxLKwTy24iCfTW5wIoj0M2O4OAEMmrldmEeb3OzJMvG9B1VSrTjmWix9f+FGYYj7rr8UCbKKS8zgJ+i/WITL0xZ4aDPGOe/h5TNe0AGgmgGJyMI/O6tMtEI0rEpuoBpbA2nmnLrtiEQq2Pd4fdwnyASrQUAQWDGgCtACrTjvVk45Nio86MuevxMGvU47TjLFmxBx9TFzuaUIwJMaC8oicPF80ZUNhJiRqkepwxdWNGkWBq/HpumEawMwMZZ+VCpVLF0/h5HZOC21uA8nqPKOvMMqGrJkBZri2gpoa36nh9naYdu3M5R931eKDxoAhoToDi5HUMVpq6FvFRb0Ab11z6LHErEJnvgWaS/J44dhmNubueq3igUbaww/nIEe+5pNoWsLLjKuV9b6J8neI+obQmGS0vujYWlKQxWI/kXndAvW1QFJotV7hTOkfb9XigMBO7ugyVBWhkAZplmQ0oBUPplA0o2dFYx001o9bkZQwMRNwHGuF4/DxeyxpP+SqC5cJ12xbtKgFKrZAiK42xC4B6vXiZduy1oOPsejzMcDvZcaMEaMbVOC1VS4bQJpQAtAAVjlV7IKYhJDIixGohMZ9kITPTHbrGMH9k1g/lPw0CNHYAtZ9d37iuoKNtEdTutIySc3dAZdqxNdl47F2PB5qTVLKgKcHFYSHoqxRAYSRoHuvV7Lh3s2xBrSZpLs3REqO4q7cO+gOuCCjfGS1oSg4O36UWUBCEE1sknoJeLqD2tGOebDz2rsfDtUG50sRIT5pl2hrG4rJAI5L2j494hB0ZQYzZG0ABYA7y416zNFxZD6tLLEZzC9AIFgtfR7zmKO5YH0c4wkkAleaBr8wF0EqovhzBXxigbWS/ux4PM9zOHuW2EhDEtSbYYl59kUd0ErQcgKcqOpdOdR1+B9cfMmJ2a+ynxWhqAIUAqg5dqQwrWhZKlnKIYbRVWlPm6xVG+G0UWRJeYiIZlJZRco4D6J53PR5ojXob0BW1K9dOf2ScJWw32X2PtQcl285pQPU+dBJO5QGisZdRHL2X0Qg+uSMP5kNAc3qiGB8wXaHPUwU0rgN0tegqfnoZZridHoSMX8zIeVsSduZzvdASnlK1cV4G1HRhRtjlpEcw69iTymcNy1/rGcgaUB3IQkAjWofEATR2fgA8e6Jrc5fSAOiE0i/MVBGydHQs9qsMaMJ7yBI6GJhE90gDiqcptsm5k0gP4Itze4AUEW5PZzbToOLYVpTnRWECXUlSZBE/JvyDxpif2fo5kfvrkmFe0hzlmgJa+WMlo0bHBKjTA49k6bapmcfBgMYQw4ReJ70BWCI+VG74lCFLqdUcBTOsp93TAFTr3rg3Q2Tt7An7iWa2BcXGgh66bH6O++scC2o1SPubs1FyXkMLWl6jvk4q78LxdJlPTWgqbVEb0FgDKpUxVNu62ZqVAOWAU2rWcNZuPa55J32iLqBodV1AdT3OgHoDoPW9ntcN0NlL8xr1dVJ5Fzagsd6URmynblIaQPWYe67cHUBlrH0mDk/E40DjlFoHzgrOCOl6bdX3GlB5LhvQTDdYdQ9A9ad4nSUq9ADoqNK8BHidVN4F+SZwxChSRZ3ZGLqAUkUuVbz0y9MMEb0tZ8T97VFOaeI0t25PA5I1o0lsLU3K40a9gNpVfKUPSfTJuniZ2ykaAB1ZhgQUj7j8cEFlAlR6hjSgsAAT2y9wlABQCpISodq7SmNerZ4BhS2783Wimqtm/wTLrSdyU7MSFPVUwYojlICSZD4v3kgJUHwQp9ADoKNK8xr1dVJ5F6ZWlPJLY6hq17xgZ4K9jAQomD7s8sTwkK7fAQracQaDQmAuyR1nQEUScfUFZMut138FOv6E6+okFsE7AzoILaPkvH6AVtaor5PKu9CAyjqMikPcDi7XfZga0Ah3AonZxrmAYuWM8z2BLNMdytHNOJFNwGQGSZZxm9LU9OKMmVi+XKIFF5Py2t+RdqJWAdC5yUBhplxXfFKbwxYIaz3gg/hMUp5tmcssTmoCCJ84mJO9b4ohcROBR5TAYNCMNl3I9O6eWRypxm7utEaTzGF0zdvXMb+mlo5WHMYvAWp+GPZRWQ5TAHRkGQrQ3DZK7MdEcWwBSoONNYo0uqkMKNTkWJtnOM/NBTQlQNn14tR4n8TpZEJIc97ZxvLt9bL0AihqXeUOoCUpe/QB0JHFAhR6/aG3s0cVL24Nl3BiTVrTgLK10wPitAnNeYSHyqS+6xnLiQbUTA3lfT4F0JhRXsNZCZUaRiOz+uhavHVrFXCmj5sYujlrfpWOUwVApxMD6EbxCTHQXk4SzDU2ZUsWFJ1yDsXbgNKin1CLY39RnsAc0DX3UUZZIj5/xlU4A5pnbteUAEqBTxkKnWUlQyq7M8nwEgVpQtX9KmoG1HQgBECnEw0oLg6Ke4S26EqqvIvEARSXh4+hYk64qyhbrzgMKY5MlJd3nokgag5MKQ8qI3dIAMWgZxZBFJScJABaPPu1HS6A9mluGKW2BjtnmlFy77UBzXPZcD4AOjuxVxYhK9orDgoDmkoGVACFoFLOzU2zr7yuxfOELCiMUo5o7GWOi4xFeuEcDWiUYRsUCQPsEgE0wsVMCFCm3RN7wniTBaluyorLbjtISQB0FuICisazD6AcvtFfLEBJ1mZOuoY0TvRETJgQB3scUmTUByjiBi2FVAOKqEKHAHRZmQCAgOWYUT1xGYa0JJFBNLIQxEaG/Ahqg+6JllFyXi9AYTg9dcOf9ajiHTtDTjIDyW45A4okI5vo8UTYmUmwxGa2mw5v4tZImQEUnSUGFCd+0gUKBTiAAl9R5sae9OR6fFTdJHUe3jh68mu2LB6W9qKliwRASZT1xCbo5mj7xI/Ku/AAGgufJBJtlCqeARU3B1qFMc3RkEAog5lk1oafMMiEAHV2/c7IaCZJogdHIaAU7fIxqsBb6yHPTYBmzYDay+J2oKWLBEBZziDCdHW/zRST8qvQfYn0jQGNZdgdN+1MyzPGPboiXtBWLChEOSMTGNUW096RlqJNuqWABloZYdNc5J4pZBy3tkGL7YE0MzuGaVvKZj7mVvKKAvUNtPAkvM60dJEAaHepvIuSKwEiLUhuQ0qzkzyjHC1oRsM9GdCYAU1ox65IBtuZKR8JD9Oj74ke1GQqdmi/4rAn0sswW8OebJ9JHHeL0Uw78+wslcP3AdCRZQxAeV9C2CNGekJX3M3uAgpxUBnOlOMKOlmkParUmgfPk0XwioxMFvIjcuHN8BLcvc44TTy2iswop4LnteyoAbTSwVR4JrD2oKWLBEC7S+VdlADVFXvMhi1hQMWERRagUa5hSmU/IwA0cmxekq35k6CL9GhTATSmlmktoGYlPXoiaY4CjMSmMGoAzRoATXegZZScAVBLxPgYiVdk3hIeRYSjRIU4spKRXqeGN/8w3UQZuTgQ2OdJddGavCPqKoqRtnVEZlAA5Zo+dgUApQGn9gIQjstENnyl7Whsd3muLJsaAB1ZBgLUVI9a2ARxjQ7Vb0xm0QBqYpbQRYQz4u3zOQFK69IBsBl0vFMLcc1tA66o9fqgNpfcmkxoahzaV1yex7XNprsenji3V8s3PygAOpUMZUFXSVwaZu4CiobNGvnJ/Tf5KtUtAQQ0pi29EDm2oFFid0FRe1Z3w2P/EE5iStCA4vlINytiWclZA5qVe+s1o2u9Tk5UYdQAqlfx6dD+rNLSRQKgKLJqQ7/V7RSfeXkiBLUYXUAlFsSWEgCF4l5LzYyhdW5UMqCpNpIWoIkBFI0zKBLIeb08CbmWAF3BX0VmhjhLAxlbowwo/4DIMKoBTcyvTK3f6ut0aqKliwRAu0v1ZWCHo3uK251UpDkv8C2DLwnQdU4d4muEN+OJ7BKqLwMqdTdWxy6gqvoHB8mkoQVrxVRy/z1DlJuRIeTb2VW9DejKbpM2AWpj3YqWLhIA7S7Vl4F7GrinGNBcVkdKdYsT9zQG0YCi2eO2J60tyo1REw8y7cuUCc1wmUXt5HM7NpdgqwUoW9CVLHWXaa101UE0tQFdaUYxO/8mlNT81ADo3mSgBWxjriFtcQBdCRsJrf6BdnIdcb977gCqR9dj5WwATSRqiSPzUrS4gCFNlM9ymrScr9cVQLm5AE+RgQXFH8Ar6kprlLg23fUs+OhWm9QQmpqfGgDdm5T2i+eVnbdJ+VXQnI5SIVmtT8tCiRlURY9BI5zyhhVuYi8ywq5OLrNFci1sMdcpLOq9ojZBlsSyM1iO6+oRoVkJ0IS62g2g7EhpRldO7Km8dkPFt7dfQGiD7kfKC9ie0UYjW6TyLupdWosqSgIbcNIQIQR0LcF53QZQ6FEzEubCJbKdt1kNDHa9UZlTmv2RyLinzAAqKz/GBlAKtlOIyLGgMErQXgBiZUHq+Tn1jHaiZZSc1xLQ81s9ByzXzTMnIVOFhwioCDYI9bJ1xKf4JBEucse1faQXos1pWyYd04cFGdCC5muJ1NN6jGaBG90ZJDFMF1C08KY9urI6mnyF3o/RAGg/cddmQjrtKR9PX7nzwjulo8Lf1Rk37TMkTUBwcbRHbHcdZRJAyjSgtAqjjAmBIZ7iRIEFXZEF1W3FGCtZjo9aroxIamukH4CLhq8Sgl63PLgFvPKbUcnZUN37JQDaTxxAYV7S2ZE13u7Za/eKt190j0Aq70I5LW0ABTDTCp1oDgVQHdXhgBSGmnCcaCIT3Ig2AhQMMLRBk5wBxR1pcXNwHb2k9kXOWu2igydJbAfH7HuH2arheP3TtzGalP5IAqD9pDnM9PT7bxVPvvOWcwRSeRcRWCOfnxDZC+IQL/YwjAzboEAoRdgT3dHI6+CsLECpvueN4FMGNCNAxY9WN6r0/0jjFJ4jgpVv8oJ0r7hz03VxfK1RzWjpp9fb0ADoMNIM6JPvvlM8/d7rztFXlJTTSWMvLl/AknaS8dLGhW5C4k6JZEKLvKD2qLrMI5AYrgwWSi5oHTJYWhluzZ46j4nKdYQTAC0yvWJ4UQig+DzqroUAaj8zYSjPyYwWlDm1L7YUVNopxy6Sj6ZpbPGszWRFmT5/QbA0RyCVP9Y1tecqYkKJK72OsWtBI3Gz2U/nnh/cVF4AlUUZrU268B4Jhdxl2mdOWzKS7yNT5NmCspkkZY4FBcnLcQhrAYhVahjtYM7GtaDeIFdLC3puLdXxxU/tfblKW3Rd3ccwecOUi/KeXvBd3bIhxVaP3F3dDlLbCzf4LCiI7xXFlZckjrH3lYpDTSkoECR+erTCgSMMKJy3lpzPcXF6vIdsdBOLF8XcrbJV7gOU1zCRFSE0oHDFAZRw1oyq88Lo1ninyMEAenX/WxqSEj4VQE80JX7xbDrXfMtugFa3427dBl15wkziFftfqQOoJJKFkty1dHR4KcOIf4SrKeM7555JcrD0HBDJo6duSAOEJk9lFQvqB9S49bjGqHbr2yLqymwB3dx841gqzVaANvTkjA7os9de1l78y01evO89alhAnIk93NNDC8zZhBpAwYLGNLUy1oBGDKjcWQDFVfHBZ9I6qT1K+yHSTVLSTG0J24vnxNWYEjdInGqg6ta3lf0BauLDjRnrCDh77ucw2/zi9g+xAj8BhKAyv0U0wSEDYQGqE/zo6Oi5R4Qd7IoMW3ce4xKecL+bbzz/qvp2cgaz2c9vMaAXePIUB9Dd+NHNh6KC1gYrr7tU3cjL+QOh6CeYzuY4qO892oCaaRPWwggo0gTF5LKJPFbb2coFNGdAEwY0N4CuVvYsZ11olhXmB7EB1dzVlK95/tj8DRWa0daVfc3rGTRnfwsKSEClfXHMRKr/aYmuUzmUxZAY0LObD+Es/H9xTMcWoLQAyM2Hcj+4tLmpN5iHM8fqdioFaL68SzdAFYrjs8r+CEONB/W9R5tPTWjGiCb6tWZRJsUP1NFKSTmizIAmsEjiygwv4jYnA7qCuCh1F9EEIx7el9M9+CaUlSw3AJqYfeVsg+5/ft0cpV/piY9ulbkCukE4T2lZLgZU6mH1adssdpLUGTy7AQpPNYX8+ZtHgqG5JBt3u1fwJgpUrUKZ1tsV9AYbUd/4Hm1AMw1oLrFzPdaIRgxDiCg3thZGkdCRGaBJQ5wLPpfKBt0cIDVee6bZi/V4TvWNV5q3GsjbV523eutXK6ns25vRuQIKNss2ghodoumcbNYJASr1Lw7XEDAVW3YVj6GgGw+cU6p6pxrephntsuLWqDj3rBkyCqDaubZcH+q4XJUBlRQ5EWPcGyvAtI5TGt5EgKLzEpva3G9Bub7uCGjptMtokvJztKF0poByxamMYQ2gVr93K0Av7954UDKqgOHPeKNiH6BaxdlRdQekykZe/daob/keV7YxzSVy6VjQlUZWcuAaHwQUnVivZPgdBaKEEw2gsaAgfQDN/adNXyjPmxM76gXEOT3TniSKGala1wbUruKtqKcFKK+CSLX17YcWjcjupmRBr+7/4W3NvFyRdoJWsbn5P6sBguY46PCAsqGTcuXZFzaPqxVvXMPFm5iFbvRGN+BzG5saWYBigpUzhNgBdM2AlvZRKEstoIVmlL6n9Yy6tnWegPKubepDjKF4RlztX91XwDFCBtCykwTMwKJJTPTFsQGU2q/nYhstQC/v3mIniVT4t0BqDjMNDyi3LG0Xo4KKZVJBYm0XxaWhqBDTSUnLgFq31I2F1Ur6zrc+aw7jUXzOU0H3smr6dWWqiNzjAAC94BDo+Y1X0WqqStYTZmITZwB1wky3CpqL8e9eQvbOVPofm1qf6m3RYwPqhJmUClhVsbrN4QwALcqh/EzY0GcSnLhJUzYIUJg0wpSIDxTrarxeextAqfVA/o/PguKIFO3W0x19jB4CoDuKJzBfk7DqnreUsat4lkZAQVxr5AIqXZ6WGcvKFtQvMjyu/ln5DyPnGUd1gGayR23JZXI8+0Nog+4mrQE9b+HY+GV0J4lEA+oWsiHC3kcLLWpOuTJrZXsabGLSVTpbLbEnv/UAVPczuDlLoaeVhLno6VM76ZIBxdB8TxknDuoTU+aWBZVq3VpPVjrkVwKoHviUWuFVkAZA88gxyVsANXH4RD9GTc7YaY+u2JCCxV+5gfxrCegIMi9ArQ56TmRwlA5zfYa2SrTuWA8orgbR6lnFgub2Cg4NgJJiB9FU29FrX8WPIPvs6myWNoBGFQvK00PyOHG6Grd0BHUGdMWA0n1NH4AvJwZwyYzyn4gVe9JaA6D9ZCILypaRJgi5i9sn1jAOP6BoTC1Ak22Alhu6LZ6VAE0sQLO6nJTI2xy1hqEEQPvJpFV8XdHpkrZap7YBjHE9eyE02QpoUxvUGRRoZaC/gko0v/qwVprKH4IZC5cHQPvJLAElk+Upbhas4mPOiUZ361gP9y5lQK06nNubnQC1EsX1jG57xDrpAajvxfpL8m/9sgNRA4sFKC4scnTk6bCvyBDvcSugqzJWWnTfZ+VLrbQGlA81oKXbVh62ohrd+opV3oFRrbIpjFbW5mSk23jlgAC9OD7BwVd91maqvo4W7zCvNy1Z1AgooJ2wyjZ0upLUAYoA2MbUI35AfUNJS4gWDT+3WboDml9LQHEw89mtfpvJVl5HuxdZuywcjxZqWAtCMbEu7G9thQeLiFQAzbsBWidW16vO2A/RXoDmq+sGKBlOAHS/ffHOi7SnZjhXIufDJ8lqbQHaUO5ldrcAChvkNDxyh1/pIqozdma0M6D5NQYUZL+A2vXhVkD9d+BPe1WP9oBijVx+VgfQBtXd/ww1olbGjogW+j7eeENV5P7XC1AzlmqvVXypxeYDNKpz32XWBn1b7wBotUXA5b+1Eu5eT/Cd3YxdGO3hJJUy0jevHA6gej7+fp2kFoCC+CxoaTSds3BXhyqen9VTj+/DguJ9fcavvc8UAGU5w3GpOF10m2x/HXVStV/dANVOu7OyXFJT1HUhqHEBxXs3MLol67SA8nThycQO1OOU+zabHQ/VBq3P2MKCWjlpDPNsLShlrGlFbmd0JEBV2fsA9c20HFOm60lqzOh1ktw26MoBtP72iQe5KQBd1dT1Wyv7vXZ12nxahOrrF1/9ZgB0W4ISrRVAmyP1BlBrAH6WbQnI18nOv7KR0f2o5G9e2QLo1Q/+fEZVfHvZ9T06pTQMoLLHTEV8gPotaBsZgJa6iFEdo1MCen4ypzboeMKRl5YSbU9CAivflkWMU1FaTdeTdDyJ/S8gtx52JGlug1587dFhArr977VZWlhQq96ujdqXctZV2hNY0OojVzI2mdGWo6u3yO5e/PlRy0lq+5PZA1o/6G43QHtKu5ye5/VkrO0bchmdENBiXmGm9rL9dTTLXixord+zbeXMLjIkoKva4dKOZx8A7S7bX0fv9yjSo4q3xMkyOqA+k1+XsbaTXSM6LaBTy/UEtKZJsD3jVmkPaOkJ6jM2jQPpPPSpXqW/JAOgXd/jIDknBrRHxi2I9qI0ANqlBNq+xyFy1npV+1O5e8ZaQ1r0RTQA2q0E2r3HIXLqKrbOb5ojoLWIUs4ejAZAu5ZAm/c4aM7DAhSliqjk7IxoALRXCQyXsSegqbMmydAqd89YNqRWzm6MBkB7lsBQGa8poLWzmUC6+EwB0N4lMEzGawsoika0nLP7UHz65pUA6HYZu0F4IIDuHiANgO5aArtljK87oG0QbaI0ALp7CeySsS+gjsrmgH5TzrEy1sfwt1X2AdBhSmBKQLd1OdXn7K2yR84GO9pU2QdAhymBAOj2nA2A1lf2AdBhSqBPxqbGWQeVW/tEa3P2VrmfnF5EOwJ65AF002a97b3KgQIKMoAFrR8LDZI25Oytco85y4x2A9Q75eP2Q9kFdio5fED7TM9spzLtnXPIjF1yuoZ0d0BB+u/BNYgEQOsl7Z1zyIwdc/YFtGZefBEsaAB0Xzm7A3pUART2he2FyFBywICu9gmo7CSTds7ZW+XgOYfx4i9fmpTQAGi9pL1zDplxakCLs0lnzR0+oPvLmI6vctCcuwO6ee5RsKDTF12dpOOrHDTnABb0vOVyh/uTAOhSVPpLskUVP60EQJei0l+SAdCu73GUnEtU6S/JQwf06St3XngHj558+86de3J6j+9xlJxLVOkv4AMH9Nlr94q3X4Sjp997vXjyx6/z+T2+x1FyLlGlv4QPHNCn33+rePKdt9TR54Dpm2JC9/geR8m5RJX+Ej5wQJ989x20nSR09BUle38sR5Jx1S1KDhzQz1+wAH322styfo9/6B6pXX/+cM3ZBCr9JXzAgL55586LtgV9+ormMwB6gCr9pXzAgIKYNqjy4u+Z83t8j2Vp2g3+cGmZQKW/hA8cUKjVyYt3+DxQC2oGm09OywQq/SV84IByHFQZ0bfvgEzjxQ8E6BDLvgdAx5ZD6EkKgA6R0V+SAdCu79EjwwBqzyibnJYJVPpLMgDa9T3uLWf9dLK9qRwk46SAwv7CYZePvhmDyi4Z/SWpkfROmoPByhfPhwHLo+dcokp/STYDuoEZnWHKR9+MQWWXjP6SbAYUJEz5mLzolqHSX5JbAb26P+lWnQHQxaj0l+Q2QC/vTstnAHQxKv0ludWLn9aHD4AuR6W/JJsBnZ7PAOhiVPpLshlQ2i8+ePGj51yiSn9Jbqnip5cA6FJU+ksyANr1PY6Sc4kq/SUZAO36HkfJuUSV/pIMgHZ9j6PkXKJKf0kGQLu+x1FyLlGlvyQDoF3f4yg5l6jSX5IB0K7vcZScS1TpL8kAaNf3OErOJar0l2QAtOt7HCXnElX6SzIA2vU9jpJziSr9JRkA7foeR8m5RJX+kgyAdn2Po+Rcokp/SQZAu77HUXIuUaW/JC0ofZvJTi8B0KWo9JfkVkAn3qozALoYlf6S3Abo5jC34w5yXWQLoGc3Xg0WdHLbsgyV/pLUdB45u8maBKGKn77orkXkFQAACLZJREFUlqHSX5KhDdr1PY6Sc4kq/SUZAO36HkfJuUSV/pIMgHZ9j6PkXKJKf0kGQLu+x1FyLlGlvyT/1i8mQQB0+qJbhkp/SW4FdGoJgC5Fpb8kA6Bd3+MoOZeo0l+SAdCu73GUnEtU6S/JAGjX9zhKzh1UxnE8tsphMvpLMgDa9T2OkrO/yjjuSejkv9JfkgHQru9xlJwB0IORAGg3CYCOLAHQjjmvWRt09hIAXYrKgcEZSwKgS1E5MDhjSQB0KSoHBmcsCYAuReXA4IwlAdClqBwYnLEkALoUlQODM5YEQJeicmBwxpIA6FJUDgzOWBIAXYrKgcEZSwKgS1E5MDhjSQB0KSoHBmcsCYAuReXA4IwlAdClqBwYnLEkALoUlQODM5YMt7rdVwa7U1A5tcoZSQA0qJy1BECDyllLWMA2yKwlABpk1hIADTJrCYAGmbUEQIPMWnYA9Okrd154xz5yP/YhFZVPvn3nzr2iePvOnTu/99Y4KlnXiL8SNMLP3OOvnK/0B/TZa+qVvWgduR/7kIrKp997vXjyx68Xb97biz6fStY15q8E+VyBur9fOWPpD+jT779VPPnOW+bI/RjuERtUfg6l9+a9Z3/x+j7UeVWyrjF/JRypv8Q9/soZS39An3z3HXxv+sj9GO4RG1TCSfWhakKs6cdQybpG/pVgR/f4K2cs/QGFSofeHh+5H8M9YoPKAqrBl7GW35N9qahkXeP+Svxnj79yxnLoFvTpKy/zxf200LxGW+ka91d+rv2xxbVDD7sNqrx4XWD7KTpfgxB0jdsGfVP+CgOg7QXqVnE2Xyb33f7Yh1RUMp9gYJ79l73QUlHJusb8lVyx7/FXzlh2joPCX/jIcVCj0ooQ/u6eGmeVX8m6RvyV0pbY46+cr4SepCCzlgBokFlLADTIrCUAGmTWEgANMmsJgAaZtQRAg8xaDhjQ8yMlp0Vx8fwDOWUd2t8v796Cj83N+q3Pv/hpJTPJ2XOPUBfc4er+rWoCvuvV/SOUE/emQXaUgwX06j6AsVFAtAEUQG4C1A9nIZmu7n8LOL28e1KTAJLgtYvj0xY3DdJaDhZQ5uL8uUdtAP3Drz3qCSgSd/nSj19SCTY3PKlcQIszY2QDoAPIoQIqPBTEAVSwt+DwR0dHYOsujrG21YCenp0QSpAQiFJG9cartx9yQvj35OL5P8ebnt98KKlE0ea5n8OH+mvQmm7/UKWAu/yoCqh108K6VZAecqiAKubkUFEIjUP4/+L4uUfwiVcVaAbQCwXjBsi7hZxBZX15FwgzCdX/ROCJpMLbw6fC+1ydOjspjKZbVOXDXSAZA7pRjQn7ps6tgnSXQwUU0QMbdeOBOsRqVv2D9bH6/ptHlMQAWpyfQIINWc9T/FQM2QnNh6RCTYj1qTKZSLOjSe4CycRJUqftmzq3CtJdDhVQKXPiYIPVujq8LThsjphdSXz5EnB3zq422jRMbRKiJT4Bc3duO+QqIyQUKyya4M7mLmxBL44pj7npedW3D9JFDhVQafL5Ab28e+NByYKqOhoAteptMoo3bNPJjU23Sj47geqdP5oBxRq+sG8aavcd5VABlZqVAQX3esNtTgF241jQ4uoHyp0RP1xqajsh/H/5EnhOrrd+fuvsRH84muQukIr/Ys5uspXlm3od/yDt5WABRRSUxcIwU9lJAiwujl1AVVpoTSp41FVxbyQhpMDEZ+CiSypSdPFbeBf60JrgC3QAlJwkcJ7sm7q3CtJZDhdQbOhJT5IdZsL6WLUBf6xajTagiDQkvPGAwkwQIOKEACYZRQzpS6qCcmM1jagVRtODwtylMG2Oc9XetG7q3ipIZzlgQHeWTWgfzl8WCihUut6e9SAzk4UCiuGfwOcByFIBDXIgEgANMmsJgAaZtQRAg8xaAqBBZi0B0CCzlgBokFlLADTIrCUA2ku+eO/x4w++bJ/+ow/h34/rc3z0WMkv/deu3v/S/XZlJ/xUZXz3M3PZf5Oa0/OXAGgfuXrvV8XVpx+2T/8+MHb5i4YEX8Jdf+29eFFWtLFPfKQyffH4195rNVkOSQKgfQRRu1Qm9O/R7MFX9f/mlx9/ePUxmjN1/hNMucGPiw8//YRAwwsXvyguFVKffnb53uN3f00J4F/FGt1w88v3Hn9o7qvyfsznP/7w4sMLdQwsf/xreRST5ALuT4n55qRRnufgJADaSz764Ffw8emHX1599Bnipf7/9N0vi48QPzhvm8PNJ+pksfmMLwB07/9aWU2V+p80YMXmF3LDj37h3Ffl+2UBJ0CDuosCWf1HUOt/OYm6IEd4c36Uj/zGef4SAO0nm/fe/1VxqXiBylMRhNx8BnAqofOWyfr0s6v3gB2+oAD9u81nF58Um3f/kRNAE/SDL/mGWOGr/HJfgOs3v/kINahvcFldQgOKabgGhyTUXsXEeHPW6DZjD0kCoH3lnx7/GrFQ/wBBxA019TaAm12nfvwlYccXrj64/GTz2acq128+BoC0G8Q3vMSPT6z7fvz4/b97jEfqf6jVlU2mViWZxk8/4SRwjY7o5qyxofk7cwmA9hCyk59+gjx99Bk2Fd/9Eoghs1n2SCjB//tQLly9//e/3vzDh4AVWUDhh294Ub4vnGcN6iTV/GRACW11zSThI7o5a6z4WQcjAdAecvXeP6LnfAFNQgTm6uMPEc4L1Xh87zOoVzcfmPSIx6fvf1LIhY8/UI1QVQlTU9PwwzdU54tP7fuqPFcfkbd0wYoeW+R9odoPJokc4c1Z4+ZAXaQAaD/54v3Hj9/9FdblypW/eu/xB39PLUbVmAQnfiNRUrao8O/FY3NB+S8bMJofc0NA80PXP/qH90r3/fjxu3/3C2mTbh4T6pxBZflVIUk26MTDEd+c7rg5VCc+ADpDqYuH2nK4JrGjBEDnJ9s9msvHH2xLcl0kABpk1hIADTJrCYAGmbUEQIPMWgKgQWYtAdAgs5YAaJBZy/8HF/H6s4XxQawAAAAASUVORK5CYII=" /><!-- --></p>
<p>The above plots estimate the general sentiments of users based on
positive and negative words in the content. Ideal expectations would be:
an article with more positive words has a positive sentiment index and
more shares, and vice versa. Exceptions may be found in peculiar
cases.</p>
<p>Plotting the number of shares based on the number of images and
videos that an article has, based on what day of the week it is:</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(newspopData, <span class="fu">aes</span>(<span class="at">x=</span>day, <span class="at">y =</span> shares<span class="sc">/</span><span class="dv">1000000</span>)) <span class="sc">+</span> </span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">fill =</span> <span class="fu">as_factor</span>(num_imgs)), <span class="at">stat=</span><span class="st">"identity"</span>, <span class="at">position=</span><span class="st">"dodge"</span>) <span class="sc">+</span> </span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_fill_discrete</span>(<span class="at">name =</span> <span class="st">'Number of Images </span><span class="sc">\n</span><span class="st"> in Article'</span>) <span class="sc">+</span></span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">subtitle =</span> <span class="st">'Number of Images in article v/s number of shares'</span>,</span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">'Number of Images'</span>,</span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">'Number of shares (in millions)'</span>,</span>
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="fu">toupper</span>(<span class="fu">str_replace_all</span>(params<span class="sc">$</span>data_channel, <span class="st">"_"</span>, <span class="st">" "</span>)),</span>
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a> <span class="at">caption =</span> <span class="st">"Source : News Popularity Dataset"</span>) <span class="sc">+</span> </span>
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">plot.caption =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">9</span>, <span class="at">color=</span><span class="st">"red"</span>, <span class="at">face=</span><span class="st">"italic"</span>, <span class="at">hjust =</span> <span class="dv">1</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>This plot shows the number of shares (in millions) based on number of
images included in each articles for each day of the week. The general
trend might see a increase in sharing of articles over Fridays and
weekends when there is more leisure time. Moreover, tech based articles
can also see a rise in share if any new technology is released in middle
of the week. Entertainment and Lifestyles articles can see a rise in
share if any events happened over the weekend which is the case most of
the times. World and social media Articles should not have a trend, as
events from all over the world keep on happening throughout the week and
updates are posted on social media non stop. Business articles would be
more trending during working days.</p>
<p>in addition to that, not all articles would be oriented towards
images. For example, tech and business based articles readers would not
care about the number of images included, but only the content itself,
while for entertainment, world, lifestyle articles, images are one of
the major factors in capturing user’s attention, and thus, shares.</p>