Towards building a competitive opinion summarization system

Lloret, Elena; Balahur, Alexandra; Palomar, Manuel; Montoyo, Andrés

doi:10.3115/1620932.1620945

Cited by 15 publications

(7 citation statements)

References 12 publications

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“…Sentiment classification is the task of identifying the sentiment polarity (e.g., positive or negative) of * 1 Corresponding author a natural language text towards a given topic (Pang et al, 2002;Turney, 2002) and has become the core component of many important applications in opinion analysis (Cui et al, 2006;Li et al, 2009;Lloret et al, 2009;Zhang and Ye, 2008).…”

Section: Introductionmentioning

confidence: 99%

Imbalanced sentiment classification

Zhou

Wang

et al. 2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead. In particular, a novel clusteringbased stratified under-sampling framework and a centroiddirected smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different datasets shows the effectiveness of both the under-sampling framework and the smoothing strategy in handling the imbalanced problems in real sentiment classification applications.

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Section: Introductionmentioning

confidence: 99%

Imbalanced sentiment classification

Zhou

Wang

et al. 2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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“…This weight will determine which sentences are to be selected and extracted. The effectiveness of such individual modules for summarization has been shown in previous research , (Lloret and Palomar, 2009). Figure 6 depicts the summarization process, the stages of which are next explained in more detail.…”

Section: Text Summarizationmentioning

confidence: 75%

Towards a unified framework for opinion retrieval, mining and summarization

et al. 2012

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The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained shows the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.

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“…In 'opinion summarisation', sentences have also been grouped based on the feature discussed, to generate a summary of all the reviews for a product that minimised repetition (Hu and Liu, 2004). There has also been interest in the summarisation of subjective content in discussions (Hu and Liu, 2004;Lloret et al, 2009;Galley et al, 2004).…”

Section: Related Workmentioning

confidence: 99%

Summarising the points made in online political debates

Egan¹,

Siddharthan²,

Wyner³

2016

Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

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Online communities host growing numbers of discussions amongst large groups of participants on all manner of topics. This user-generated content contains millions of statements of opinions and ideas. We propose an abstractive approach to summarize such argumentative discussions, making key content accessible through 'point' extraction, where a point is a verb and its syntactic arguments. Our approach uses both dependency parse information and verb case frames to identify and extract valid points, and generates an abstractive summary that discusses the key points being made in the debate. We performed a human evaluation of our approach using a corpus of online political debates and report significant improvements over a highperforming extractive summarizer.

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Towards building a competitive opinion summarization system

Cited by 15 publications

References 12 publications

Imbalanced sentiment classification

Imbalanced sentiment classification

Towards a unified framework for opinion retrieval, mining and summarization

Summarising the points made in online political debates

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