A unified graph model for sentence-based opinion retrieval
Social Media Content Analysis: Natural Language Processing and beyond, 2018•World Scientific
There is a growing research interest in opinion retrieval as on-line users' opinions are
becoming more and more popular in business, social networks, etc. Practically speaking, the
goal of opinion retrieval is to retrieve documents, which entail opinions or comments,
relevant to a target subject specified by the user's query. A fundamental challenge in opinion
retrieval is information representation. Existing research focuses on document-based
approaches and documents are represented by bag-of-word. However, due to loss of …
becoming more and more popular in business, social networks, etc. Practically speaking, the
goal of opinion retrieval is to retrieve documents, which entail opinions or comments,
relevant to a target subject specified by the user's query. A fundamental challenge in opinion
retrieval is information representation. Existing research focuses on document-based
approaches and documents are represented by bag-of-word. However, due to loss of …
There is a growing research interest in opinion retrieval as on-line users’ opinions are becoming more and more popular in business, social networks, etc. Practically speaking, the goal of opinion retrieval is to retrieve documents, which entail opinions or comments, relevant to a target subject specified by the user’s query. A fundamental challenge in opinion retrieval is information representation. Existing research focuses on document-based approaches and documents are represented by bag-of- word. However, due to loss of contextual information, this representation fails to capture the associative information between an opinion and its corresponding target. It cannot distinguish different degrees of a sentiment word when associated with different targets. This in turn seriously affects opinion retrieval performance. In this chapter, we propose a sentence-based approach based on a new information representation, namely topic-sentiment word pair, to capture intra-sentence contextual information between an opinion and its target. Additionally, we consider inter-sentence information to capture the relationships among the opinions on the same topic. Finally, the two types of information are combined in a unified graph-based model, which can effectively rank the documents. Compared with existing approaches, experimental results on the COAE08 dataset showed that our graph-based model achieved significant improvement.
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