Abstract
An unsupervised sentiment analysis method is presented to classify user comments on laptops into positive ones and negative ones. The method automatically extracts informative features in testing dataset and labels the sentiment polarity of each feature to make a domain-specific lexicon. The classification accuracy of this lexicon will be compared to that with an existing general sentiment lexicon. Besides, the concept of three-way decision will be applied in the classifier as well, which combines lexicon-based methods and supervised learning methods together. Results indicate that the overall performance can reach considerable improvements with three-way decision.
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Zhou, Z., Zhao, W., Shang, L. (2014). Sentiment Analysis with Automatically Constructed Lexicon and Three-Way Decision. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_71
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DOI: https://doi.org/10.1007/978-3-319-11740-9_71
Publisher Name: Springer, Cham
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