Modeling the Helpful Opinion Mining of Online Consumer Reviews

• Main Authors: Yi-Ching Zeng, 曾議慶
• Other Authors: Shih-Hung Wu
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/15559389945324826271

碩士 === 朝陽科技大學 === 資訊工程系碩士班 === 101 === In the recent researches of text mining and opinion mining, finding the polarity of an opinion is a hot topic. However, the reason why a user gives a positive or a negative opinion is more interesting in the same context. In recent years, with the rapid growth of the Web, gathering information from other user’s comment becomes a necessary step on decision making for people or organization. To mine the reason behind the opinion, we would like to distinguish the sentences as showing emotion with “Helpful” or “Less-Helpful”. If the sentences content the reason, we think that the author is serious to write the reviews. Our research can help the user and company quickly to understand why people like or dislike something and remove noisy reviews. Finding helpful reviews is important. Helpful reviews can give the readers ideals. Noisy reviews just waste time for watching, so reading only helpful reviews not only see reason but also understand quickly. The first step of the research is to create an experiment corpus. We collect Amazon review include Books、Digital_Camera、Computer、FoodsDrinks、Movies、Shoes、Toys and Cell-Phone eight classes. We manually define the sentence types as the one with “Helpful” and “Less-Helpful”. Connors’s paper defines the “Helpful” and “Less-Helpful” features. His paper use 10 features to analysis but it’s not automatically. We implement 8 features. The features are "Pros and Cons"、"Product Usage Information"、"Detail"、"Comparisons"、"Lengthy" and "Use of Ratings". The overall accuracy of three-class problem is about 73%. Helpful negative reviews can be found with 82% precision and 77% recall. Helpful positive reviews can be found with 74% precision and 64% recall. Less-Helpful reviews can be filtered out automatically from all the consumer reviews with a high recall rate about 87% and 73% precision. Second experiment is finding most useful feature. “Detail” is most important of all.