Product Review Sentiment Classification using Parts of Speech A Case Study of Textbook Reviews
A prospective buyer interested in a particular item may find out information about the item from various sources, including product reviews. With interactive information sharing facilitated by Web 2.0, a lot of product reviews are available on the web. For a popular item with a large number of reviews, a prospective buyer could use some help in selecting only reviews of interest, such as, only positive or negative reviews, when only particular kind of information is being sought for. This research work implemented a system that classified a product review as having either positive or negative tone, through the analysis of parts of speech of the reviews textual content. The system used machine learning algorithms for training positive-negative classification models. Experiments were performed particularly on textbook reviews.
sentiment classification mining textbook book review parts of speech machine learning
Patrawadee Tanawongsuwan
Computer Science Department School of Applied Statistics National Institute of Development Administration Bangkok Thailand
国际会议
成都
英文
424-427
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）