Twitter Sentiment Analysis for the Recent Trend Extracted from the


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 10, pp. 731-738, Oct. 2013
10.3745/KTSDE.2013.2.10.731,   PDF Download:

Abstract

We analyze public opinion via a sentiment analysis of tweets collected by using recent topic keywords extracted from newspaper articles. Newspaper articles collected within a certain period of time are clustered by using K-means algorithm and topic keywords for each cluster are extracted by using term frequency. A sentiment analyzer learned by a machine learning method can classify tweets according to their polarity values. We have an assumption that tweets collected by using these topic keywords deal with the same topics as the newspaper articles mentioned if the tweets and the newspapers are generated around the same time. and we tried to verify the validity of this assumption.


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Cite this article
[IEEE Style]
G. H. Lee and K. J. Lee, "Twitter Sentiment Analysis for the Recent Trend Extracted from the," KIPS Transactions on Software and Data Engineering, vol. 2, no. 10, pp. 731-738, 2013. DOI: 10.3745/KTSDE.2013.2.10.731.

[ACM Style]
Gyoung Ho Lee and Kong Joo Lee. 2013. Twitter Sentiment Analysis for the Recent Trend Extracted from the. KIPS Transactions on Software and Data Engineering, 2, 10, (2013), 731-738. DOI: 10.3745/KTSDE.2013.2.10.731.