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 can not distinguish different degrees of a sentiment word when associated with different targets . This in turn seriously affects opinion retrieval performance . In this paper , 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 . In recent years , there is a growing interest in sharing personal opinions on the Web , such as product reviews , economic analysis , political polls , etc . These opinions can not only help independent users make decisions , but also obtain valuable feedbacks ( Pang et al . , 2008 ) . Opinion oriented research , including sentiment classifica-tion , opinion extraction , opinion question answering , and opinion summarization , etc . are receiving growing attention ( Wilson , et al . , 2005 ; Liu et al . , 2005 ; Oard et al . , 2006 ) . However , most existing works concentrate on analyzing opinions expressed in the documents , and none on how to represent the information needs required to retrieve opinionated documents . In this paper , we focus on opinion retrieval , whose goal is to find a set of documents containing not only the query keyword(s ) but also the relevant opinions . This requirement brings about the challenge on how to represent information needs for effective opinion retrieval . In order to solve the above problem , previous work adopts a 2-stage approach . In the first stage , relevant documents are determined and ranked by a score , i.e. tf-idf value . In the second stage , an opinion score is generated for each relevant document ( Macdonald and Ounis , 2007 ; Oard et al . , 2006 ) . The opinion score can be acquired by either machine learning-based sentiment classifiers , such as SVM ( Zhang and Yu , 2007 ) , or a sentiment lexicons with weighted scores from training documents ( Amati et al . , 2007 ; Hannah et al . , 2007 ; Na et al . , 2009 ) . Finally , an overall score combining the two is computed by using a score function , e.g. linear combination , to re-rank the retrieved documents . Retrieval in the 2-stage approach is based on document and document is represented by bag-of-word . This representation , however , can only ensure that there is at least one opinion in each relevant document , but it can not determine the relevance pairing of individual opinion to its target . In general , by simply representing a document in bag-of-word , contextual information i.e. the corresponding target of an opinion , is neglected . This may result in possible mismatch between an opinion and a target and in turn affects opinion retrieval performance . By the same token , the effect to documents consisting of mul-tiple topics , which is common in blogs and on-line reviews , is also significant . In this setting , even if a document is regarded opinionated , it can not ensure that all opinions in the document are indeed relevant to the target concerned . Therefore , we argue that existing information representation i.e. bag-of-word , can not satisfy the information needs for opinion retrieval . In this paper , we propose to handle opinion retrieval in the granularity of sentence . It is observed that a complete opinion is always expressed in one sentence , and the relevant target of the opinion is mostly the one found in it . Therefore , it is crucial to maintain the associative information between an opinion and its target within a sentence . We define the notion of a topic-sentiment word pair , which is composed of a topic term ( i.e. the target ) and a sentiment word ( i.e. opinion ) of a sentence . Word pairs can maintain intra-sentence contextual information to express the potential relevant opinions . In addition , inter-sentence contextual information is also captured by word pairs to represent the relationship among opinions on the same topic . In practice , the inter-sentence information reflects the degree of a word pair . Finally , we combine both intra-sentence and inter-sentence contextual information to construct a unified undirected graph to achieve effective opinion retrieval . The rest of the paper is organized as follows . In Section 2 , we describe the motivation of our approach . Section 3 presents a novel unified graph-based model for opinion retrieval . We evaluated our model and the results are presented in Section 4 . We review related works on opinion retrieval in Section 5 . Finally , in Section 6 , the paper is concluded and future work is suggested . In this work we focus on the problem of opinion retrieval . Different from existing approaches , which regard document relevance as the key indicator of opinion relevance , we propose to explore the relevance of individual opinion . To do that , opinion retrieval is performed in the granularity of sentence . We define the notion of word pair , which can not only maintain the association between the opinion and the corresponding target in the sentence , but it can also build up the relationship among sentences through the same word pair . Furthermore , we convert the relationships between word pairs and sentences into a unified graph , and use the HITS algorithm to achieve document ranking for opinion retrieval . Finally , we compare our approach with existing methods . Experimental results show that our proposed model performs well on COAE08 dataset . The novelty of our work lies in using word pairs to represent the information needs for opinion retrieval . On the one hand , word pairs can identify the relevant opinion according to intra-sentence contextual information . On the other hand , word pairs can measure the degree of a relevant opinion by taking inter-sentence contextual information into consideration . With the help of word pairs , the information needs for opinion retrieval can be represented appropriately . In the future , more research is required in the following directions : ( 1 ) Since word pairs can indicate relevant opinions effectively , it is worth further study on how they could be applied to other opinion oriented applications , e.g. opinion summarization , opinion prediction , etc . ( 2 ) The characteristics of blogs will be taken into consideration , i.e. , the post time , which could be helpful to create a more time sensitivity graph to filter out fake opinions . ( 3 ) Opinion holder is another important role of an opinion , and the identification of opinion holder is a main task in NTCIR . It would be interesting to study opinion holders , e.g. its seniority , for opinion retrieval .

Challenge: Existing approaches to the opinion retrieval task represent documents using bag-of-words disregarding contextual information between an opinion and its corresponding text.
Approach: They propose a sentence-based approach which captures both inter and intra sentence contextual information combined with a unified undirected graph.
Outcome: The proposed method outperforms existing approaches on the COAE08 dataset showing that word pairs can represent information for opinion retrieval well.