TREC-2006 at Maryland: Blog, Enterprise, Legal and QA Tracks

Oard, Douglas W.; Elsayed, Tamer; Wang, Jianqiang; Wu, Yejun; Zhang, Pengyi; Abels, Eileen G.; Lin, Jimmy; Soergel, Dagobert

doi:10.21236/ada463747

Cited by 17 publications

(19 citation statements)

References 13 publications

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“…Then heuristic opinion detection is used to re-rank the documents. One major method to identify opinionate content is by matching the documents with a sentiment word dictionary and calculating term frequency [6,10,11,19]. The matching process is often performed multi-times for different dictionaries and different restrictions on matching.…”

Section: Related Workmentioning

confidence: 99%

“…The matching process is often performed multi-times for different dictionaries and different restrictions on matching. Dictionaries are constructed according to existing lexical categories [6,10,19] or the word distribution over the dataset [10,11,19]. Matching constraints often concern with the distance between topic terms and opinion terms, which can be thought of as a sliding window.…”

Section: Related Workmentioning

confidence: 99%

“…where α and β are combination parameters, which are often tuned by hand or learned to optimize a target metric such as binary preference [10]. Other alternatives include demoting the ranking of neutral documents [11].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval

Zhang

2008

Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

104

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Opinion retrieval is a task of growing interest in social life and academic research, which is to find relevant and opinionate documents according to a user's query. One of the key issues is how to combine a document's opinionate score (the ranking score of to what extent it is subjective or objective) and topic relevance score. Current solutions to document ranking in opinion retrieval are generally ad-hoc linear combination, which is short of theoretical foundation and careful analysis. In this paper, we focus on lexicon-based opinion retrieval. A novel generation model that unifies topic-relevance and opinion generation by a quadratic combination is proposed in this paper. With this model, the relevance-based ranking serves as the weighting factor of the lexicon-based sentiment ranking function, which is essentially different from the popular heuristic linear combination approaches. The effect of different sentiment dictionaries is also discussed. Experimental results on TREC blog datasets show the significant effectiveness of the proposed unified model. Improvements of 28.1% and 40.3% have been obtained in terms of MAP and p@10 respectively. The conclusion is not limited to blog environment. Besides the unified generation model, another contribution is that our work demonstrates that in the opinion retrieval task, a Bayesian approach to combining multiple ranking functions is superior to using a linear combination. It is also applicable to other result re-ranking applications in similar scenario.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval

Zhang

2008

Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

104

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show abstract

“…Oard et al [33] adopt passage retrieval; both dictionary-based and machine-learning based sentiment term selection methods; a passage sentiment score is calculated by using all the sentiment terms in that passage.…”

Section: Related Workmentioning

confidence: 99%

Opinion retrieval from blogs

Zhang

Meng

2007

Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management

112

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Opinion retrieval is a document retrieval process, which requires documents to be retrieved and ranked according to their opinions about a query topic. A relevant document must satisfy two criteria: relevant to the query topic, and contains opinions about the query, no matter if they are positive or negative. In this paper, we describe an opinion retrieval algorithm. It has a traditional information retrieval (IR) component to find topic relevant documents from a document set, an opinion classification component to find documents having opinions from the results of the IR step, and a component to rank the documents based on their relevance to the query, and their degrees of having opinions about the query. We implemented the algorithm as a working system and tested it using TREC 2006 Blog Track data in automatic title-only runs. Our result showed 28% to 32% improvements in MAP score over the best automatic runs in this 2006 track. Our result is also 13% higher than a state-of-art opinion retrieval system, which is tested on the same data set.

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“…In the first stage, documents are ranked by topical relevance only. In the second stage, candidate relevant documents are re-ranked by their opinion scores [16,13]. The opinion scores can be acquired by either machine learningbased sentiment classifiers, such as SVM [29], external sentiment dictionaries with weighted scores from training documents [5,1,15], exhaustively computed query term-opinion word proximity scores [25,31], or external toolkits such as OpinionFinder [5].…”

Section: Introductionmentioning

confidence: 99%

A unified relevance model for opinion retrieval

Huang

Croft

2009

Proceedings of the 18th ACM Conference on Information and Knowledge Management

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Representing the information need is the greatest challenge for opinion retrieval. Typical queries for opinion retrieval are composed of either just content words, or content words with a small number of cue "opinion" words. Both are inadequate for retrieving opinionated documents. In this paper, we develop a general formal framework-the opinion relevance model-to represent an information need for opinion retrieval. We explore a series of methods to automatically identify the most appropriate opinion words for query expansion, including using query independent sentiment resources. We also propose a relevance feedback-based approach to extract opinion words. Both query-independent and query-dependent methods can also be integrated into a more effective mixture relevance model. Finally, opinion retrieval experiments are presented for the Blog06 and COAE08 text collections. The results show that, significant improvements can always be obtained by this opinion relevance model whether sentiment resources are available or not.

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TREC-2006 at Maryland: Blog, Enterprise, Legal and QA Tracks

Cited by 17 publications

References 13 publications

A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval

A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval

Opinion retrieval from blogs

A unified relevance model for opinion retrieval

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