A Corpus-Based Method for Product Feature Ranking for Interactive Question Answering Systems

摘要

At times choosing a product can be a difficult task due to the fact that customers need to consider many features before they can reach a decision. Interactive question answering (IQA) systems can help customers in this process, by answering questions about products and initiating a dialogue with the customer when their needs are not clearly defined. For this purpose we propose a corpus-based method for weighting the importance of product features depending on how likely they are to be of interest for a user. By using this method, we hope that users can select the desired product in an optimal way. For the experiments a corpus of user reviews is used, the assumption being that the features mentioned in a review are probably more important for a person who is likely to purchase a product. To improve the method, a sentiment classification system is also employed to distinguish between features mentioned in positive and negative contexts. Evaluation shows that the ranking method that incorporates this information is one of the best performing ones.