Abstract
This paper proposes a new method of sentiment analysis for Twitter. Tweets contain various expressions; e.g., use of emoticons. The usage of these expressions links to the user’s identity and individual characters. Handling these characteristics is useful for the sentiment analysis. We focus on writing styles of each user. In this paper, we define three types of writing style; formal and two informal expressions. First, our method classifies each tweet into the three types. Then, it generates classifiers for each writing style. We apply our method to a positive / negative classification task of tweets. In the experiment, the accuracy of our method increased by approximately 3 points as compared with some baseline methods.
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Maeda, H., Shimada, K., Endo, T. (2012). Twitter Sentiment Analysis Based on Writing Style. In: Isahara, H., Kanzaki, K. (eds) Advances in Natural Language Processing. JapTAL 2012. Lecture Notes in Computer Science(), vol 7614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33983-7_28
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DOI: https://doi.org/10.1007/978-3-642-33983-7_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33982-0
Online ISBN: 978-3-642-33983-7
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