Abstract
Due to the explosive growth of user-generated contents, understanding opinions (such as reviews on products) generated by Internet users is important for optimizing business decision. To achieve such understanding, this paper investigates a discriminative approach to classifying opinions according to sentiments. The discriminative approach builds a model with the prior knowledge of the categorization information in order to extract meaningful features from the unstructured texts. The prior knowledge includes ratio factors to reinforce terms’ sentiment polarity by using TF-IDF, short for term frequency-inverse document frequency. Experimental results with four datasets show the proposed approach is very competitive, compared with some of the previous works.
| Original language | English |
|---|---|
| Pages (from-to) | 749-758 |
| Number of pages | 10 |
| Journal | Neural Processing Letters |
| Volume | 51 |
| Issue number | 1 |
| Early online date | 11 Sep 2019 |
| DOIs | |
| Publication status | Published - 01 Feb 2020 |
| Externally published | Yes |
Bibliographical note
Funding Information:This research was supported by Research Foundation of Education Bureau of Hubei Province with Grant No. D20172502. We thank the peer reviewers for great comments.
Funding Information:
This research was supported by Research Foundation of Education Bureau of Hubei Province with Grant No. D20172502. We thank the peer reviewers for great comments.
Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Keywords
- Natural language processing
- Sentiment classification
- Term weighting
- TFIDF
ASJC Scopus subject areas
- Software
- Neuroscience(all)
- Computer Networks and Communications
- Artificial Intelligence