Opinion and entity mining on web content.

摘要

One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. We study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. We propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms.;In this thesis, the problem that we also discuss is the assignment of entities that have been talked about in each sentence. If the sentence contains the product names, they need to be identified. We call this problem entity discovery. If the product names are not explicitly mentioned in the sentence but are implied due to the use of pronouns and language conventions, we need to infer the products. We call this problem entity assignment . In this thesis, we propose two effective methods to solve the problems. Entity discovery is based on pattern discovery and entity assignment is based on mining of comparative sentences. We also discuss another project on object and attribute coreference resolution. We show that some important features related to opinions can be exploited to perform the task more accurately. Experimental results using blog posts demonstrate the effectiveness of the technique.