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Agile-Model-Based Sentiment Analysis From Social Media
Abstract
To study state-of-the art associated to Twitter mining replica as well as prognostic analytic by means of Agile Software Engineering. To recognize sentiment analysis by means of agile knowledge. To obtain as well as analysis given repository for classifying sentiments into positive, negative and neutral emotions. Analysing of all the tweets obtained from the twitter keywords as positive, negative or neutral opinions and comparing all the keywords to judge which keyword is better, there is a requirement to improve from the conventional ways of sentiment analysis. This paper emphasizes on the implementation of an algorithm for automatic classification of text into positive, negative or neutral by fetching the live tweets from twitter server by using twitter API. Graphical representation of the sentiment for the purpose of comparison in the form of pie chart and bar graph. Scan the twitter and fetching the Live Tweets from Twitter server using Twitter4J Advance Java Interface and implementing the Stanford NLP Library (Natural Language Parsing) using Advance Java for classifying the tweets into positive, negative and neutral tweets.
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