Abstract
With the growth of Internet and various online services, tremendous amount of data are generated in real time. As a result, sentiment analysis of online reviews has become an important research problem. In this paper a novel feature selection and weighting scheme is proposed for the sentiment analysis of twitter data. The Part of Speech (POS) tagging and Bayes-based Classifier are utilized in the proposed scheme. Also, different from the existing schemes, independency of the attributes and the influence of emotional words are properly manipulated in deciding the polarity of test data. Computer simulation with Sentiment 140 workload shows that the proposed scheme significantly outperforms the existing sentiment analysis schemes such as naïve Bayes classifier and selective Bayes classifier.
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0717-17-0070), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2017R1A2B2009095), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2016R1A6A3A11931385), and the second Brain Korea 21 PLUS project.
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Wang, Y., Sun, L., Wang, J., Zheng, Y., Youn, H.Y. (2018). A Novel Feature-Based Text Classification Improving the Accuracy of Twitter Sentiment Analysis. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_72
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