In this paper, we design and develop a movie-rating and review-summarization system in a mobile environment. The movie-rating information is based on the sentiment-classification result. The condensed descriptions of movie reviews are generated from the feature-based summarization. We propose a novel approach based on latent semantic analysis (LSA) to identify product features. Furthermore, we find a way to reduce the size of summary based on the product features obtained from LSA. We consider both sentiment-classification accuracy and system response time to design the system. The rating and review-summarization system can be extended to other product-review domains easily.
|Number of pages||11|
|Journal||IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews|
|State||Published - 1 May 2012|
- Feature extraction
- natural language processing (NLP)
- text analysis
- text mining