Location Based Sentiment Analysis Of Twitter Data: A Literature

2234 WordsJan 11, 20179 Pages
Location based sentiment analysis of Twitter data: A Literature Review I.Karthika1, S.Priyadharshini2 1Assistant Professor, Department of Computer Science and Engineering, M.Kumarasamy College of Engineering, Karur. 2PG scholar, Department of Computer Science and Engineering, M.Kumarasamy college of Engineering, Karur. 2priyadharshinisivasamy93@gmail.com Abstract Big data is a concept used for collecting, storing, and analyzing large volume of data and provides decision making and also support optimization processes. Social media plays an important role in taking decisions about any products based on the reviews provided by the user. It accurately tells about the exact opinion of the user regarding the product. Twitter is one…show more content…
The next phase is the data preprocessing which involves the filtering of the tweets with proper grammatical relation. Sentiment score phase involves the scoring process. Once analysis process is completed, the comparisons are made based on location, feature and gender. II LITERATURE REVIEW Syed akib anwar et al. [1] proposed that Public sentiments are the main things to be noticed for collecting the feedback of the product. It can be done by using sentiment analysis. The twitter is the social media used in this paper for collecting the reviews about any product. The reviews collected are analyzed based on the locations, features and gender. There are four steps involved in the paper: Data extraction which involves collecting the twitter data, data processing involves filtering out the redundant tweets and non grammatical relations, implementation involving the product analysis using sentiment score and result involves comparison between gender, feature and locations. Xing Fang et al. [2] discusses that Sentiment analysis is a technique used for categorization of the product based on the reviews of the user. The categories of the product are good, bad or neutral. In this paper, the general problem of the sentiment polarity categorization has been resolved. The sentiment polarity categorization consists of two phases: sentence level categorization and review level categorization. The sentence
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