Support Vector Machine Based Sentiment Analysis Process for Twitter Streams
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 4
Abstract
Abstract: Sentiment Research in twitter is quite tough due to its short length. Attendance of emoticons, slang words and misspellings in tweets compelled to have a preprocessing pace beforehand feature extraction. There are disparate feature extraction methods for accumulating relevant features from text that can be requested to tweets also. But the feature extraction is to be completed in two periods to remove relevant features. In the early period, twitter specific features are extracted. Next these features are removed from the tweets to craft normal text. Later that, once more feature extraction is completed to become extra features. This is the believed utilized in this paper to produce an effectual feature vector for analyzing twitter sentiment. As no average dataset isobtainable for twitter posts of electronic mechanisms, we crafted a dataset by accumulating tweets for a precise era of time. By acting EmotionResearch on a specific area, it is probable to recognize the impact of area data in selecting a feature vector. Disparate classifiers are utilized to do the association to find out their impact in this particular area alongside this particular feature vector. This paper prepossess an SVM established EmotionResearch procedure for twitter streams
Authors and Affiliations
Rekha Malik , Sugandha Hooda , Jyoti Bharadwaj
Keywords
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