Opinion mining from online hotel reviews – A text summarization approach

Hu, Ya-Han; Chen, Yen-Liang; Chou, Hui-Ling

doi:10.1016/j.ipm.2016.12.002

Cited by 250 publications

(133 citation statements)

References 45 publications

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“…They show that the fuzzy based summarization system can successfully enhance the quality of the generated summaries. Hu et al (2017) discuss a clustering based summarization method for opinion data (Opinosis) where they suggest that the clustering of reviews can play a major role in coverage of reviews related to every instance. Wang et al (2017) discuss nine heuristic based methods for sentence extraction from long documents.…”

Section: Other Methodsmentioning

confidence: 99%

A Review on Text Summarization Techniques

Verma¹,

Verma²

2020

JSR

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In recent years, an enormous amount of text data from diversified sources has been emerged day-by-day. This huge amount of data carries essential information and knowledge that needs to be effectively summarized to be useful. Hence, the main contribution of this paper is twofold. We first introduce some concepts related to extractive text summarization and then provide a systematic analysis of various text summarization techniques. In particular, some challenges in extractive summarization of single as well as multiple documents are introduced. The problems focus on the textual assessment and similarity measurement between the text documents are addressed. The challenges discussed are generic and applicable to every possible scenario in text summarization. Then, existing state-of-the-art of extractive summarization techniques are discussed that focus on the identified challenges.

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Section: Other Methodsmentioning

confidence: 99%

A Review on Text Summarization Techniques

Verma¹,

Verma²

2020

JSR

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“…These global optimization-based techniques have generated better results vis-à-vis greedy techniques, for abstractive summaries based on standard data sets, e.g., DUC. Although ATS has been used for summarizing from multiple sources, such as patents (Tseng et al 2007;Codina-Filbà et al 2017), biomedical text (Reeve et al 2007), research papers (Lloret et al 2013), IMF country reports (Ackermann et al 2006), product reviews (Zhan et al 2009;Hu et al 2017), court decisions (Moens 2007), product news (Chakraborti and Dey 2015), it has not been used specifically for extracting summaries from corpora created with the intention of gathering information on multiple aspects of a business organization's competitors. The conceptual framework proposed in Chakraborti and Dey (2014) and Chakraborti (2015) proposing ATS as a component for creating summaries from CI corpora lacks the support of any empirical analysis that shows the effectiveness of ATS for generating useful system summaries.…”

Section: Automatic Text Summarizationmentioning

confidence: 99%

Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization

Chakraborti

Dey

2018

Bus Inf Syst Eng

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Automatic text summarization can be applied to extract summaries from competitor intelligence (CI) corpora that organizations create by gathering textual data from the Internet. Such a representation of CI text is easier for managers to interpret and use for making decisions. This research investigates design of an integrated system for CI analysis which comprises clustering and automatic text summarization and evaluates quality of extractive summaries generated automatically by various text-summarization techniques based on global optimization. This research is conducted using experimentation and empirical analysis of results. A survey of practicing managers is also carried out to understand the effectiveness of automatically generated summaries from CI perspective. Firstly, it shows that global optimization-based techniques generate good quality extractive summaries for CI analysis from topical clusters created by the clustering step of the integrated system. Secondly, it shows the usefulness of the generated summaries by having them evaluated by practicing managers from CI perspective. Finally, the implication of this research from the point of view of theory and practice is discussed.

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“…Others made polarity classifications based on a lexicon [42]. Hu et al [43] identified the top sentences from hotel reviews after calculating the sentence importance and the similarity between sentences. Hu and Chen [44] extracted positive and negative sentiments from each review and used them to predict review helpfulness.…”

Section: Content Analysismentioning

confidence: 99%

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

Ren

Hong

2017

Sustainability

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With the development of Web 2.0, many studies have tried to analyze tourist behavior utilizing user-generated contents. The primary purpose of this study is to propose a topic-based sentiment analysis approach, including a polarity classification and an emotion classification. We use the Latent Dirichlet Allocation model to extract topics from online travel review data and analyze the sentiments and emotions for each topic with our proposed approach. The top frequent words are extracted for each topic from online reviews on Ctrip.com. By comparing the relative importance of each topic, we conclude that many tourists prefer to provide "suggestion" reviews. In particular, we propose a new approach to classify the emotions of online reviews at the topic level utilizing an emotion lexicon, focusing on specific emotions to analyze customer complaints. The results reveal that attraction "management" obtains most complaints. These findings may provide useful insights for the development of attractions and the measurement of online destination image. Our proposed method can be used to analyze reviews from many online platforms and domains.

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Opinion mining from online hotel reviews – A text summarization approach

Cited by 250 publications

References 45 publications

A Review on Text Summarization Techniques

A Review on Text Summarization Techniques

Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

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