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(This article was first published on <strong><a href="https://fordoxblog.wordpress.com/2017/03/12/rsentiment/"> R – FordoX</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Every system needs continuous improvement. Feedback, positive or negative, plays an important role in that improvement. Humans are fairly instinctive in interpreting the tone of the feedback. But, to teach a machine to understand the same, is highly complex. Various algorithms and tools are available today to automatically identify and categorize opinions of any textual feedback.</p>
<p>The application of sentiment analysis is wide and useful. It gives us a wide overview of opinion regarding various topics. Sentiment analysis provides the ability to quickly understand the impact of any product or system and react accordingly.</p>
<p>In one of my works, I applied sentiment analysis to predict the opinion of students regarding various academic dimensions of an institute. It is published at <a target="_blank" rel="nofollow" href="https://link.springer.com/chapter/10.1007/978-981-10-3156-4_26">Springer</a>. I used R for the purpose and was playing around with various packages already existing at CRAN but none of them was working according to my need. So, I conceptualized a tool, which applies text mining techniques to elicit insights from textual data and  published it as an open source package (<a target="_blank" rel="nofollow" href="https://cran.r-project.org/web/packages/RSentiment/vignettes/Introduction.html">RSentiment</a>) to CRAN.</p>
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<p>The algorithm uses Parts of Speech tagging to tag each word in the sentence. I designed various cases on sequence of occurrence of various parts of speech. I checked if there is any adverb or adjective bearing positive score following any negative quantifier  “not” , “no” and accordingly assigned score to it. The package categorizes sentences into 5 categories like:</p>
<pre><code>Very Negative
Negative
Neutral
Positive
Very Positive</code></pre>
<p>There are currently 3 methods in the package:</p>
<pre><code>calculate_score: calculates score of a sentence or a collection of sentences as 
text.

calculate_sentiment: classifies a sentence or a text into a sentiment category.

calculate_total_presence_sentiment: calculates total number of sentences 
in each sentiment category.</code></pre>
<p>I am working on it for further improvements. Any feedback will be highly appreciated. My package might be able to benefit from it. <img alt="😉" class="wp-smiley" src="https://i0.wp.com/s0.wp.com/wp-content/mu-plugins/wpcom-smileys/twemoji/2/72x72/1f609.png?w=456&ssl=1" data-recalc-dims="1"></p>
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