Product Feature Extraction and Sentiment Analysis in Product Reviews
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Team Members
Project Title
Product Feature Extraction and Sentiment Analysis in Product Reviews
Project Abstract
In this project, we plan to work on product reviews of various product classes and analyze them for finding the product features and opinion of various customers about those features. Using this analysis we aim to identify feature-wise good and bad aspects of a given product. This can be a useful practical solution to allow customers to help decide how well a product satisfies his/her needs if they are only looking for few important features in a product and don't care about other features.
Datasets
Amazon product reviews data set -
- http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#datasets
- Amazon product review dataset for various classes
Apart from that, we have already built our own web crawlers and extracted some more product reviews from www.amazon.com to train our system.
Baseline
Related Papers
- Pang, B., L. Lee, and S. Vaithyanathan. 2002. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, 79–86.
- Turney, P. D. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, 417–424.
- M. Hu and B. Liu. Mining Opinion Features in Customer Reviews. In Proceedings of Nineteenth National Conference on Arti?cial Intelligence. 2004.
- Dave, K., Lawrence, S., and Pennock, D.M. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. WWW 2003.
- Jianxing Yu , Zheng-Jun Zha, Meng Wang, Kai Wang, Tat-Seng Chua, Domain-Assisted Product Aspect Hierarchy Generation: Towards Hierarchical Organization of Unstructured Consumer Reviews, EMNLP 2011.
- Bishan Yang and Claire Cardie, Extracting Opinion Expressions with semi-Markov Conditional Random Fields.
