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A Sentiment Analysis on Twitter Data

Author(s):

Vaishnavi Charde , N.I.E.T.M.Nagpur; Tejeswy Chimnekar, N.I.E.T.M.Nagpur; Shital Badole, N.I.E.T.M.Nagpur; Shubhangi Ghadinkar, N.I.E.T.M.Nagpur; Prof. B. Kumbhare, N.I.E.T.M.Nagpur

Keywords:

Data Mining, Feature Extraction Naïve Bayes Classifier, Natural Language Processing, Twitter, Sentiment, Review, Aspect, Hashtag, Entity, Emotions

Abstract

Sentiment analysis is a broad research area in academic as well as business field. The term sentiment refers to the feelings or opinion of the person towards some particular domain. Hence it is also known as opinion mining. It can be expressed in terms of polarity, reviews or previously by thumbs up and down to denote positive and negative sentiments respectively. Sentiments can be analyzed using NLP, statistics or machine learning techniques. Sentiment analysis may ask questions regarding “customer satisfaction and dissatisfaction, “public opinion towards new iPhone series launched” etc. In real world, public or consumer opinions about some product or brand are very important for its sell. Hence sentiment analysis is a very important research area for real life applications i.e. decision making. For this purpose, the methodology we use is as follows: access the twitter API to extract the tweets about elections. The extracted tweets are then processed so as to convert all letters in the lower case, to special characters etc. which would make the further tasks more efficient. We classify these processed tweets using a supervised classification approach. The classifier used is Naïve Bayes Classifier to classify the tweets as positive, negative or neutral. The classifier is trained using tweets which bear a distinctive polarity. The result can be used further to gain an insight into the views of the people using twitter about a particular topic that is being searched by the user. It can help corporate houses to devise strategies on the basis of the popularity of their product among the masses. It may help the consumers to make informed choices based on the general sentiment expressed by the Twitter users on a product.

Other Details

Paper ID: IJSRDV5I120336
Published in: Volume : 5, Issue : 12
Publication Date: 01/03/2018
Page(s): 1116-1119

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