Twitter Streaming and Analysis through R

Abstract

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(data­frame)­is­generated­with­the­tweets­and­is­converted­into­.CSV­(Comma­Separated­Values)­file­which­is­suitable­ Analysis. Sentiment Analysis 1 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,­a­negative­words­file­and­a­positive­words­file­is­taken­for­comparison­with­the­tweet­data­to­calculate­positive­ score­and­negative­score­of­the­tweet.­The­difference­of­these­scores­gives­us­with­the­final­score­of­the­tweet. Findings: The number­of­tweets­identified­as­positive­(or)­negative­(or)­neutral­so­that­the­status­of­Make-In-India­can­be­­ visualised­in­a­ 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 for­all­the­tweets­and­stored­in­a­file.­In­the­third,­we­perform­visual­analysis­from­the­stored­data­using­R­statistical­software­ to conclude the impact of the program. Application/Improvements: Application­of­the­­ methodology­is­to­get­findings 2 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.