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data_channel_is_socmed.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>SOCMED</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">$</span>data_channel] <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%>%</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>( <span class="sc">-</span><span class="fu">starts_with</span>(<span class="st">'data_channel_is_'</span>), <span class="sc">-</span>url)</span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>newspopData</span></code></pre></div>
<pre><code>## # A tibble: 2,323 × 54
## timedelta n_tokens…¹ n_tok…² n_uni…³ n_non…⁴
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 731 8 257 0.568 1.00
## 2 731 8 218 0.663 1.00
## 3 731 9 1226 0.410 1.00
## 4 731 10 1121 0.451 1.00
## 5 729 9 168 0.778 1.00
## 6 729 9 100 0.760 1.00
## 7 729 10 1596 0.420 1.00
## 8 728 7 518 0.486 1.00
## 9 727 8 358 0.503 1.00
## 10 727 6 358 0.622 1.00
## # … with 2,313 more rows, 49 more variables:
## # n_non_stop_unique_tokens <dbl>,
## # num_hrefs <dbl>, num_self_hrefs <dbl>,
## # num_imgs <dbl>, num_videos <dbl>,
## # average_token_length <dbl>,
## # num_keywords <dbl>, kw_min_min <dbl>,
## # kw_max_min <dbl>, kw_avg_min <dbl>, …</code></pre>
<h1 id="exploratory-data-analysis">Exploratory Data Analysis</h1>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(newspopData<span class="sc">$</span>shares)</span></code></pre></div>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu.
## 5 1400 2100 3629 3800
## Max.
## 122800</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 0.05.</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">337</td>
</tr>
<tr class="even">
<td align="left">Wednesday</td>
<td align="right">416</td>
</tr>
<tr class="odd">
<td align="left">Thursday</td>
<td align="right">463</td>
</tr>
<tr class="even">
<td align="left">Friday</td>
<td align="right">332</td>
</tr>
<tr class="odd">
<td align="left">Saturday</td>
<td align="right">180</td>
</tr>
<tr class="even">
<td align="left">Sunday</td>
<td align="right">137</td>
</tr>
<tr class="odd">
<td align="left">Tuesday</td>
<td align="right">458</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">2</td>
<td align="right">84</td>
</tr>
<tr class="even">
<td align="left">Monday</td>
<td align="left">4</td>
<td align="right">84</td>
</tr>
<tr class="odd">
<td align="left">Monday</td>
<td align="left">1</td>
<td align="right">99</td>
</tr>
<tr class="even">
<td align="left">Monday</td>
<td align="left">3</td>
<td align="right">70</td>
</tr>
<tr class="odd">
<td align="left">Wednesday</td>
<td align="left">2</td>
<td align="right">110</td>
</tr>
<tr class="even">
<td align="left">Wednesday</td>
<td align="left">4</td>
<td align="right">108</td>
</tr>
<tr class="odd">
<td align="left">Wednesday</td>
<td align="left">1</td>
<td align="right">100</td>
</tr>
<tr class="even">
<td align="left">Wednesday</td>
<td align="left">3</td>
<td align="right">98</td>
</tr>
<tr class="odd">
<td align="left">Thursday</td>
<td align="left">2</td>
<td align="right">124</td>
</tr>
<tr class="even">
<td align="left">Thursday</td>
<td align="left">4</td>
<td align="right">123</td>
</tr>
<tr class="odd">
<td align="left">Thursday</td>
<td align="left">1</td>
<td align="right">105</td>
</tr>
<tr class="even">
<td align="left">Thursday</td>
<td align="left">3</td>
<td align="right">111</td>
</tr>
<tr class="odd">
<td align="left">Friday</td>
<td align="left">2</td>
<td align="right">90</td>
</tr>
<tr class="even">
<td align="left">Friday</td>
<td align="left">4</td>
<td align="right">77</td>
</tr>
<tr class="odd">
<td align="left">Friday</td>
<td align="left">1</td>
<td align="right">88</td>
</tr>
<tr class="even">
<td align="left">Friday</td>
<td align="left">3</td>
<td align="right">77</td>
</tr>
<tr class="odd">
<td align="left">Saturday</td>
<td align="left">2</td>
<td align="right">61</td>
</tr>
<tr class="even">
<td align="left">Saturday</td>
<td align="left">4</td>
<td align="right">26</td>
</tr>
<tr class="odd">
<td align="left">Saturday</td>
<td align="left">1</td>
<td align="right">44</td>
</tr>
<tr class="even">
<td align="left">Saturday</td>
<td align="left">3</td>
<td align="right">49</td>
</tr>
<tr class="odd">
<td align="left">Sunday</td>
<td align="left">2</td>
<td align="right">38</td>
</tr>
<tr class="even">
<td align="left">Sunday</td>
<td align="left">4</td>
<td align="right">17</td>
</tr>
<tr class="odd">
<td align="left">Sunday</td>
<td align="left">1</td>
<td align="right">46</td>
</tr>
<tr class="even">
<td align="left">Sunday</td>
<td align="left">3</td>
<td align="right">36</td>
</tr>
<tr class="odd">
<td align="left">Tuesday</td>
<td align="left">2</td>
<td align="right">108</td>
</tr>
<tr class="even">
<td align="left">Tuesday</td>
<td align="left">4</td>
<td align="right">128</td>
</tr>
<tr class="odd">
<td align="left">Tuesday</td>
<td align="left">1</td>
<td align="right">109</td>
</tr>
<tr class="even">
<td align="left">Tuesday</td>
<td align="left">3</td>
<td align="right">113</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 src="data:image/png;base64,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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 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>