Sentiment of Sentence in Tweets: A Review
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 6
Abstract
Abstract: Determine the sentiment of sentence that is positive or negative based on the presence of part of speech tag, the emoticons present in the sentences. For this research we use the most popular microblogging sittwitter for sentiment orientation. In this paper we want to extract tweets form the twitter related to the product like mobile phones, home appliances, vehicle etc. After retrieving tweets we perform some preprocessing on it like remove retweets, remove tweets containing few words with minimum threshold of length five, remove tweets containing only urls. After this the remaining tweets are pre-processed like that transform all letters of the tweets to the lower case then remove punctuation from the tweets because it reduces the accuracy of result. After this remove extra white spaces from the tweets, then we apply a pos tagger to tag each word. The tuple after the applying above steps contain (word, pos tag, English-word, stop-word). We are interested in onlytweets that contain opinion and eliminate the remaining non-opinion tweets from the data set. For this we use the Naïve Bays classification algorithm. After this we use short text classification on tweets i.e., the word having different meaning in different domain. In order to solve this problem we use two different feature selection algorithms the mutual information (MI) and the X2 feature selection. At final stage predicting the orientation of an opinion sentence that is positive or negative as we mentioned above. For this we use two model like unigram model and opinion miner.
Authors and Affiliations
Sandip Mali , Ashish Balerao , Suvarnsing Bhable , Sangramsing Kayte
Keywords
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