journal article
Apr 17, 2020
Emo2Vec
ACM Transactions on Internet Technology
Vol. 20
No. 2
pp. 1-17
·
Association for Computing Machinery (ACM)
Abstract
Sentiment analysis or opinion mining for subject information extraction from the text has become more and more dependent on natural language processing, especially for business and healthcare, since the online products and service reviews affect the consuming behaviors. Word embeddings that can map the words to low-dimensional vector representations have been widely used in natural language processing tasks. But the word embeddings based on context such as Word2Vec and GloVe fail to capture the sentiment information. Most of existing sentiment analysis methods incorporate emotional polarity (positive and negative) to improve the sentiment embeddings for the emotion classification. This article takes advantage of an emotional psychology model to learn the emotional embeddings in Chinese first. In order to combine the semantic space and an emotional space, we present two different purifying models from local (LPM) and global (GPM) perspectives based on Plutchik's wheel of emotions to add the emotional information into word vectors. The two models aim to improve the word vectors so that not only the semantically similar words but also the sentimentally similar words can be closer than before. The Plutchik's wheel of emotions model can give eight-dimensional vector for one word in emotional space that can capture more sentiment information than the binary polarity labels. The obvious advantage of the local purifying model is that it can be fit for any pretrained word embeddings. For the global purifying model, we can get the final emotional embeddings at once. These models have been extended to handle English texts. The experimental results on Chinese and English datasets show that our purifying model can improve the conventional word embeddings and some proposed sentiment embeddings for sentiment classification and multi-emotion classification.
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Metrics
14
Citations
38
References
Details
- Published
- Apr 17, 2020
- Vol/Issue
- 20(2)
- Pages
- 1-17
- License
- View
Authors
Funding
Fundamental Research Funds for the Central Universities
Award: 11XNL010
Natural Science Foundation of China
Award: 91846204, 91646203, 61532016, 61532010, 61762082
National Key Research and Development Program of China
Award: 2016YFB1000602, 2016YFB1000603
the Science and Technology Opening up Cooperation project of Henan Province
Award: 172106000077
Cite This Article
Shuo Wang, Aishan Maoliniyazi, Xinle Wu, et al. (2020). Emo2Vec. ACM Transactions on Internet Technology, 20(2), 1-17. https://doi.org/10.1145/3372152
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