Abstract
Sentiment word identification (SWI) refers to the task of automatically identifying whether a given word expresses positive or negative opinion. SWI is a critical component of sentiment analysis technologies. Traditional sentiment word identification techniques become unqualified because they need seed sentiment words which leads to low robustness. In this paper, we consider SWI as a matrix factorization problem and propose three models for it. Instead of seed words, we exploit sentiment matching and sentiment consistency for modeling. Extensive experimental studies on three real-world datasets demonstrate that our models outperform the state-of-the-art approaches.
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Liang, J., Zhou, X., Hu, Y., Guo, L., Bai, S. (2015). Sentiment Word Identification with Sentiment Contextual Factors. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_18
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DOI: https://doi.org/10.1007/978-3-319-25255-1_18
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