Transcription of Text Mining for Sentiment Analysis of Twitter Data
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Text Mining for Sentiment Analysis of Twitter Data
Text Mining for Sentiment Analysis of Twitter data Shruti Wakade, Chandra Shekar, Kathy J. Liszka and Chien-Chung Chan The University of Akron Department of Computer Science Abstract Text messages express the state of minds from a large population on earth. From the perspective of decision makers, this collection of messages provides a precious source of information. In this paper, we present the use of Weka data Mining tools to extract useful information for classifying Sentiment of tweets collected from Twitter . The results of tweet Mining are represented as decision trees that can be used for judging Sentiment of new tweets. We introduce a new method for preprocessing tweets for decision tree learning. We evaluate the impact of tweets containing emoticons to the classifying process. The method is applied to perform Sentiment Analysis from tweets related to iPhone and Microsoft.
Sentiment analysis is a field of research that determines if there is a favorable or non-favorable reaction in text. Figure 1. Example tweet. Our approach is to use the Weka1 data mining software with a positive and negative word set and compare it to a second word set provided by Twitter. We
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