Abstract
With an increasingly large amount of sentimental information embedded in online documents, sentiment analysis is quite valuable to product recommendation, opinion summarization, and so forth. Different from most works on identifying documents' qualitative affective information, this research focuses on the measurement of users' intensity over each sentimental category. Affect indicates positive or negative sentiment, while cognition includes certainty and tentative. Thus, our research can help bridge the cognitive and affective gaps between users and documents. The contributions of this study are twofold: (i) we proposed a neural network-based framework to sentiment strength prediction by convolving hybrid vectors, and (ii) we considered words jointly with a set of linguistic features for enhancing model robustness and adaptiveness. By exploiting the auxiliary features of sentiments from the corpus, the proposed model did not rely on well-established lexicons, and showed its robustness over sparse words. Experiments on six corpora validated the effectiveness of our sentiment strength prediction method.
Original language | English |
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Title of host publication | 26th International World Wide Web Conference 2017, WWW 2017 Companion |
Publisher | International World Wide Web Conferences Steering Committee |
Pages | 5-14 |
Number of pages | 10 |
ISBN (Electronic) | 9781450349147 |
ISBN (Print) | 9781450349147 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Externally published | Yes |
Event | 26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia Duration: 3 Apr 2017 → 7 Apr 2017 |
Conference
Conference | 26th International World Wide Web Conference, WWW 2017 Companion |
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Country/Territory | Australia |
City | Perth |
Period | 3/04/17 → 7/04/17 |
Keywords
- Convolutional neural network
- Hybrid features
- Sentiment strength