Sentiment Analysis of Twitter Data Using Statistical Analysing Tool R Studio - Goyal, Radhika; Kakkar, Meenal (Indian Journal of Computer Science, 2019)
Today, social media is considered as the best platform for the exchange of data and views on a large scale. With increase in number of users, the data generated on daily basis is increasing rapidly. This data is useful in analyzing people's opinions about the ongoing trends and activities occurring across the world. The data generated on these platforms is analyzed and this is known as social media data analysis. This type of data analysis is also known as opinion mining where the views and thoughts of people are extracted and processed, which then helps in knowing the current mindset of people regarding various activities. To accomplish the need of knowing people's mindset, the popular social media site Twitter can be used. Twitter is an online micro-blogging service that facilitates delivery and interpretation of 140 posts known as "tweets". It is one of the platforms for gathering large and diverse data, and from this gathered data it is easy to analyze a person's sentiments. This is known as sentiment analysis. Sentiment analysis uses the idea of data analysis to extract emotions, comments, reviews, etc. from different social media platforms. The paper contains tweets that are extracted using the hashtag IndiaStrikeBack (#IndiaStrikeBack) for the duration of ten days when people were emotional about the response given back by the Indian armed forces against the attack. The paper then analyses the thoughts and feelings of various users of Twitter behind their tweets about the action taken by the Indian armed forces in response to the Pulwama attack. For doing the analysis of the extracted tweets, a robust tool R was used. R is a statistical tool that is user-friendly and easy to use. It is used for performing data analysis of the data that has been extracted from social media platform. With the help of packages such as Twitter, the extraction of tweets from Twitter and doing its analysis becomes easier. The extracted data is stored in Comma Separated Values (csv) files and is further represented in the form of bar charts. The opinions of the people are categorized into two different sentiments, namely, (positive and negative). There is a classification of users' emotion into 8 categories, namely, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust that are taken from NRC sentiment dictionary. The feelings transmitted by sarcasm and irony are not very well served by the instrument for analyzing feelings.