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Title:
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EMOTION ANALYSIS USING SELF-TRAINING
ON MALAYSIAN CODE-MIXED TWITTER DATA |
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Author(s):
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Kathleen Swee Neo Tan, Tong Ming Lim and Yee Mei Lim |
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ISBN:
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978-989-8704-19-1 |
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Editors:
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Piet Kommers and Guo Chao Peng |
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Year:
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2020 |
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Edition:
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Single |
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Keywords:
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Emotion Analysis, Code-Mixed, Twitter, Self-Training, Sentiment Analysis |
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Type:
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Full |
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First Page:
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181 |
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Last Page:
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188 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Microblogs such as Twitter has made available a vast resource of User Generated Content (UGC) on which emotion
analysis may be performed. Organizations increasingly value the opinions obtained from emotion analysis. These insights
help to drive decision-making activities and provide constructive inputs to engage their customers and services. In a
multiracial country such as Malaysia, it is common to find that tweets are written in mixed languages of Malay,
Malaysian slang and English. These tweets increase the complexity of the emotion analysis task, especially considering
that there is a serious lack of labeled data available in order to make use of supervised learning techniques. This paper
explores the use of self-training, a semi-supervised technique that only requires a small initial labeled dataset to conduct
emotion analysis of Malaysian code-mixed Twitter data. The results are promising as the accuracy achieved is higher
compared to the baseline models. |
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