Traditional methods of annotating the sentiment of a document are based on sentiment lexicons, which have been proven quite efficient. However, such methods ignore the effect of supplementary features (e.g., negation and intensity words), while only consider the counts of positive and negative words, the sum of strengths, or the maximum sentiment score over the whole document primarily. In this paper, we propose to use convolutional neural network (CNN) and long short-term memory network (LSTM) to model the role of negation and intensity words, so as to address the limitations of lexicon-based methods. Results show that our model can not only successfully capture the effect of negation and intensity words, but also achieve significant improvements over state-of-the-art deep neural network baselines without supplementary features. Copyright © 2018 Springer International Publishing AG, part of Springer Nature.
|Title of host publication||Web and big data: Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, Proceedings, Part I|
|Editors||Yi CAI, Yoshiharu ISHIKAWA, Jianliang XU|
|Place of Publication||Cham|
|Publication status||Published - 2018|
Deep neural networks
Long short-term memory
CitationXu, Z., Fu, Y., Chen, X., Rao, Y., Xie, H., Wang, F. L., & Peng, Y. (2018). Sentiment classification via supplementary information modeling. In Y. Cai, Y. Ishikawa, & J. Xu (Eds.), Web and big data: Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, Proceedings, Part I (pp. 54-62). Cham: Springer.
- Negation words
- Intensity words
- Sentiment supplementary information