Extracting sentiment knowledge from pros/cons product reviews: Discovering features along with the polarity strength of their associated opinions
- Publication Type:
- Journal Article
- Citation:
- Expert Systems with Applications, 2018, 114 pp. 267 - 288
- Issue Date:
- 2018-12-30
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Extracting sentiment knowledge from pros cons product reviews.pdf | Published Version | 5.36 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2018 Elsevier Ltd Sentiment knowledge extraction is a growing area of research in the literature. It helps in analyzing users’ opinions about different entities or events, which can then be utilized by analysts for various purposes. Particularly, feature-based sentiment analysis is one of the challenging research areas that analyzes users’ opinions on various features of a product or service. Of the three formats for the product reviews, our focus in this paper is limited to analyzing the pros/cons type. Due to the nature of pros/cons reviews, they are mostly concise and follow a different structure from other review types. Therefore, specialized techniques are needed to analyze these reviews and extract the customers’ discussed product features along with their personal attitudes. In this paper, we propose the Pros/Cons Sentiment Analyzer (PCSA) framework that exploits dependency relations in extracting sentiment knowledge from pros/cons reviews. We also utilize two different lexicons to ascertain the polarity strength of the extracted features based on the customers’ opinions. Several experiments are conducted to evaluate the performance of PCSA in its different phases.
Please use this identifier to cite or link to this item: