Twitter Streaming and Analysis through R

Objectives: To retrieve tweets from Twitter through Twitter API. The domain chosen for analysis is Make-In-India Dataset. Methods/Statistical Analysis: This paper consists of two phases of work: 1. Data Streaming from Twitter, 2. Knowledge Mining through R-Studio. Methods used for the two key operations are: 1. Twitter API and 2. Sentiment Analysis through R. Twitter application is created to request for connection with the Twitter database. Once connection establishes, authentication keys are generated. Providing the search key “Make-In-India” and number of keys required, a file with .df (data frame) is generated with the tweets and is converted into .CSV (Comma Separated Values) file which is suitable Analysis. Sentiment Analysis1 is also called Opinion mining talks about retrieving facts from the tweets such as how many people supporting Make-In-India (or) how many are negative with the scheme (or) how many are neutral with it. For this process, a negative words file and a positive words file is taken for comparison with the tweet data to calculate positive score and negative score of the tweet. The difference of these scores gives us with the final score of the tweet. Findings: The number of tweets identified as positive (or) negative (or) neutral so that the status of Make-In-India can be visualised in a graph. Firstly, the extraction of Tweets is from Twitter through R-Studio Environment About “Make-In-India”. Secondly, we parse the extracted raw tweets using R according to the types and store in .CSV format in R database. Scores are calculated for all the tweets and stored in a file. In the third, we perform visual analysis from the stored data using R statistical software to conclude the impact of the program. Application/Improvements: Application of the methodology is to get findings2 from the public opinions which are available from Twitter tweets on a particular government issue, political parties and medical status around the country. Also it is useful to assess the popularity of the political leader and the program. Decision making is possible through sentiment analysis of user tweets.