Whitelaw C, Garg N, Argamon S (2005) Using appraisal groups for sentiment analysis. In: 2017 7th International Annual Engineering Seminar (InAES). Ramadhani AM, Goo HS (2017) Twitter sentiment analysis using deep learning methods. At the end, a glimpse of possible future work in this field has been included to show the researcher’s way ahead with the proposed approach.ĭattu BS, Gore DV (2015) A survey on sentiment analysis on twitter data using different techniques. This article discusses the technologies employed and contains details of tackling the issue of Sentiment Analysis through the proposed approach, thus justifying the increased accuracy over existing approaches. The proposed approach has been compared with textblob API for sentiment analysis. Moreover, the classifier’s accuracy can be further increased if more words in the dictionary are added. A decent amount of accuracy has been obtained using the proposed algorithm. To determine the polarity of texts, other features have been used as per Rule Based Emotion Classification (RBEM) algorithm. The proposed approach consists of classifying the sentiments using emotions from Plutchik’s wheel of emotion, which provides eight basic emotions to make the tasks more approachable. This article addresses the issue of polarization of Twitter sentiments, which is one of the major areas of concern regarding sentiment analysis. Sentiment analysis is the task of predicting sentiments of words, sentences or entire document.
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