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data_channel_is_bus.html
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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>BUS</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">$</span>data_channel] <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%>%</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>( <span class="sc">-</span><span class="fu">starts_with</span>(<span class="st">'data_channel_is_'</span>), <span class="sc">-</span>url)</span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>newspopData</span></code></pre></div>
<pre><code>## # A tibble: 6,258 × 54
## timedelta n_tokens…¹ n_tok…² n_uni…³ n_non…⁴
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 731 9 255 0.605 1.00
## 2 731 9 211 0.575 1.00
## 3 731 8 397 0.625 1.00
## 4 731 13 244 0.560 1.00
## 5 731 11 723 0.491 1.00
## 6 731 8 708 0.482 1.00
## 7 731 10 142 0.655 1.00
## 8 731 12 444 0.601 1.00
## 9 731 6 109 0.667 1.00
## 10 730 13 306 0.535 1.00
## # … with 6,248 more rows, 49 more variables:
## # n_non_stop_unique_tokens <dbl>,
## # num_hrefs <dbl>, num_self_hrefs <dbl>,
## # num_imgs <dbl>, num_videos <dbl>,
## # average_token_length <dbl>,
## # num_keywords <dbl>, kw_min_min <dbl>,
## # kw_max_min <dbl>, kw_avg_min <dbl>, …</code></pre>
<h1 id="exploratory-data-analysis">Exploratory Data Analysis</h1>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(newspopData<span class="sc">$</span>shares)</span></code></pre></div>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu.
## 1.0 952.2 1400.0 3063.0 2500.0
## Max.
## 690400.0</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 0.03.</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">1153</td>
</tr>
<tr class="even">
<td align="left">Tuesday</td>
<td align="right">1182</td>
</tr>
<tr class="odd">
<td align="left">Wednesday</td>
<td align="right">1271</td>
</tr>
<tr class="even">
<td align="left">Thursday</td>
<td align="right">1234</td>
</tr>
<tr class="odd">
<td align="left">Friday</td>
<td align="right">832</td>
</tr>
<tr class="even">
<td align="left">Saturday</td>
<td align="right">243</td>
</tr>
<tr class="odd">
<td align="left">Sunday</td>
<td align="right">343</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">290</td>
</tr>
<tr class="even">
<td align="left">Monday</td>
<td align="left">2</td>
<td align="right">298</td>
</tr>
<tr class="odd">
<td align="left">Monday</td>
<td align="left">1</td>
<td align="right">309</td>
</tr>
<tr class="even">
<td align="left">Monday</td>
<td align="left">3</td>
<td align="right">256</td>
</tr>
<tr class="odd">
<td align="left">Tuesday</td>
<td align="left">4</td>
<td align="right">338</td>
</tr>
<tr class="even">
<td align="left">Tuesday</td>
<td align="left">2</td>
<td align="right">300</td>
</tr>
<tr class="odd">
<td align="left">Tuesday</td>
<td align="left">1</td>
<td align="right">271</td>
</tr>
<tr class="even">
<td align="left">Tuesday</td>
<td align="left">3</td>
<td align="right">273</td>
</tr>
<tr class="odd">
<td align="left">Wednesday</td>
<td align="left">4</td>
<td align="right">372</td>
</tr>
<tr class="even">
<td align="left">Wednesday</td>
<td align="left">2</td>
<td align="right">321</td>
</tr>
<tr class="odd">
<td align="left">Wednesday</td>
<td align="left">1</td>
<td align="right">276</td>
</tr>
<tr class="even">
<td align="left">Wednesday</td>
<td align="left">3</td>
<td align="right">302</td>
</tr>
<tr class="odd">
<td align="left">Thursday</td>
<td align="left">4</td>
<td align="right">351</td>
</tr>
<tr class="even">
<td align="left">Thursday</td>
<td align="left">2</td>
<td align="right">334</td>
</tr>
<tr class="odd">
<td align="left">Thursday</td>
<td align="left">1</td>
<td align="right">269</td>
</tr>
<tr class="even">
<td align="left">Thursday</td>
<td align="left">3</td>
<td align="right">280</td>
</tr>
<tr class="odd">
<td align="left">Friday</td>
<td align="left">4</td>
<td align="right">196</td>
</tr>
<tr class="even">
<td align="left">Friday</td>
<td align="left">2</td>
<td align="right">245</td>
</tr>
<tr class="odd">
<td align="left">Friday</td>
<td align="left">1</td>
<td align="right">203</td>
</tr>
<tr class="even">
<td align="left">Friday</td>
<td align="left">3</td>
<td align="right">188</td>
</tr>
<tr class="odd">
<td align="left">Saturday</td>
<td align="left">4</td>
<td align="right">7</td>
</tr>
<tr class="even">
<td align="left">Saturday</td>
<td align="left">2</td>
<td align="right">84</td>
</tr>
<tr class="odd">
<td align="left">Saturday</td>
<td align="left">1</td>
<td align="right">131</td>
</tr>
<tr class="even">
<td align="left">Saturday</td>
<td align="left">3</td>
<td align="right">21</td>
</tr>
<tr class="odd">
<td align="left">Sunday</td>
<td align="left">4</td>
<td align="right">11</td>
</tr>
<tr class="even">
<td align="left">Sunday</td>
<td align="left">2</td>
<td align="right">117</td>
</tr>
<tr class="odd">
<td align="left">Sunday</td>
<td align="left">1</td>
<td align="right">154</td>
</tr>
<tr class="even">
<td align="left">Sunday</td>
<td align="left">3</td>
<td align="right">61</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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" /><!-- -->
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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" /><!-- --></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 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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>