Sentiment Analysis for Thai dramas on Twitter
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Abstract
Since most consumers are interested in watching TV series and using online media such as Twitter to exchange opinions about them, there are a lot of comments are found and the consumers must take more time for reading and understanding the overall messages of the other consumers' views. Therefore, this research has studied about word grouping, classification of the sentiments of the text about Thai TV series called Thai dramas or Lakorn. The objective is to analyze the opinion messages expressed as like, dislike and neutral comments and the scope of this research is collecting texts about the dramas in Thai language, but it does not cover slang, misspellings, and dialects. Moreover, the implemented web application for analyzing opinions about Thai dramas on Twitter is developed to help analyzing and summarizing the preferences for Thai dramas. All words of collected Thai messages will be looked up in the vocabulary list created for the Thai dramas. Then, the word vectors of messages are generated for training a learning model using the Naïve Bayes approach. After that, the model will classify the comments about the dramas, whether most consumers like or not like the drama. The developed system is expected to be a tool that will be able to make decision watching the dramas easier and this will be beneficial to the drama producers to facilitate planning the production of the dramas in the future.
Keywords: Sentiment analysis, Thai dramas, Naïve Bayes
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