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Analytic‐based product opinion detection algorithm for twitter microblogging network
Author(s) -
M Nirmala,
Babu M. Rajasekhara
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3881
Subject(s) - sentiment analysis , microblogging , sarcasm , computer science , popularity , social media , ambiguity , product (mathematics) , polarity (international relations) , opinion leadership , rumor , artificial intelligence , natural language processing , information retrieval , world wide web , linguistics , irony , psychology , sociology , mathematics , social psychology , social science , philosophy , cell , genetics , biology , geometry , programming language , public relations , political science
Summary Twitter, with an ever‐increasing user base, has greatly influenced the opinion and purchase habits of the common masses. This has in turn forced the product firms to get involved with sentiment analysis which enables them to mine the actual opinion about their product and make business decisions accordingly. Even though a majority of the existing methods detect sentiment of the tweet with a reasonable accuracy, few ignore emoticons while others consider them as stop words. Emoticons have enabled the users to express their emotion more accurately which eliminates the ambiguity that can arise with usage of words. The trending popularity of emoticons among the users combined with its ease of usage makes it highly lucrative in sentiment analysis. Hence, mining the product opinion without considering the emoticons will severely undermine the accuracy and reliability of the opinion. Moreover, sarcasm detection is still an uncharted territory in opinion mining and is exceedingly difficult to factor it in. Sarcastic tweets when left undetected will affect the accuracy of the opinion. Therefore, the polarity of the individual words and emoticons of the tweets are computed using linguistic analysis. The sarcastic tweets are then classified and eliminated based on their anomalous polarity. By placing a higher emphasis on emoticons, the proposed emoticon‐based linguistic opinion algorithm yields satisfactory results when compared with other traditional and state of the art approaches.