Summarising customer online reviews using a new text mining approach

Aleebrahim, Neda; Fathian, Mohammad

doi:10.1504/ijbis.2013.054468

Cited by 11 publications

(3 citation statements)

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“…Web mining has also been used in other areas of social science. AleEbrahim and Fathian ( 2013 ) develop a method to summarize customer online reviews from websites. Al-Hassan et al ( 2013 ) investigate whether the North American Industry Classification System code (NAICS) effectively shows the true industrial sectors of Fortune 500 firms by analyzing their websites.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of web mining in studying innovation

2014

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As enterprises expand and post increasing information about their business activities on their websites, website data promises to be a valuable source for investigating innovation. This article examines the practicalities and effectiveness of web mining as a research method for innovation studies. We use web mining to explore the R&D activities of 296 UK-based green goods small and mid-size enterprises. We find that website data offers additional insights when compared with other traditional unobtrusive research methods, such as patent and publication analysis. We examine the strengths and limitations of enterprise innovation web mining in terms of a wide range of data quality dimensions, including accuracy, completeness, currency, quantity, flexibility and accessibility. We observe that far more companies in our sample report undertaking R&D activities on their web sites than would be suggested by looking only at conventional data sources. While traditional methods offer information about the early phases of R&D and invention through publications and patents, web mining offers insights that are more downstream in the innovation process. Handling website data is not as easy as alternative data sources, and care needs to be taken in executing search strategies. Website information is also self-reported and companies may vary in their motivations for posting (or not posting) information about their activities on websites. Nonetheless, we find that web mining is a significant and useful complement to current methods, as well as offering novel insights not easily obtained from other unobtrusive sources.

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Section: Literature Reviewmentioning

confidence: 99%

Use of web mining in studying innovation

2014

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“…Concerning approaches producing sentiment-based summaries initially focused mainly on blogs (Balahur-Dobrescu et al, 2009;Missen et al, 2009;Mithun, 2010); There is also a high number of approaches addressing the summarization of reviews (Zhuang et al, 2006;Lerman and McDonald, 2009;AleEbrahim and Fathian, 2013;Ravi Kumar and Raghuveer, 2013;Raut and Londhe, 2014), forums (Carbonaro, 2010;Ren et al, 2011) as well as microblogs (Sharifi et al, 2010;Harabagiu and Hickl, 2011;Chakrabarti and Punera, 2011;Bahrainian and Dengel, 2013). We also find Farzindar (2014), focusing on how summarization tasks can improve social media retrieval and event detection, and Li et al (2014), in which the multi-document summarization by sentence compression is explored.…”

Section: Related Workmentioning

confidence: 99%

A novel concept-level approach for ultra-concise opinion summarization

Lloret

Boldrini

Vodolazova

et al. 2015

Expert Systems with Applications

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The Web 2.0 has resulted in a shift as to how users consume and interact with the information, and has introduced a wide range of new textual genres, such as reviews or microblogs, through which users comunicate, exchange, and share opinions. The explotation of all this user-generated content is of great value both for users and companies, in order to assist them in their decision-making processes. Given this context, the analysis and development of automatic methods that can help manage online information in a quicker manner are needed.Therefore, this article proposes and evaluates a novel concept-level approach for ultra-concise opinion abstractive summarization. Our approach is characterized by the integration of syntactic sentence simplification, sentence regeneration and internal concept representation into the summarization process, thus being able to generate abstractive summaries, which is one the most challenging issues for this task. In order to be able to analyze different settings for our approach, the use of the sentence regeneration module was made optional, leading to two different versions of the system (one with sentence regeneration and one without). For testing them, a corpus of 400 English texts, gathered from reviews and tweets belonging to two different domains, was used. Although both versions were shown to be reliable methods for generating this type of summaries, the results obtained indicate that the version without sentence regeneration yielded to better results, improving the results of a number of stateof-the-art systems by 9%, whereas the version with sentence regeneration proved to be more robust to noisy data.

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“…To analyze such qualitative information, recent studies have applied various text mining techniques. One of the most common marketing applications of text mining techniques is using consumers' product reviews to uncover complex consumer preferences (Aggarwal et al, 2009;AleEbrahim and Fathian, 2013;Decker and Trusov, 2010;Lee and Bradlow, 2011;Netzer et al, 2012). Moreover, text mining analyzing consumers' online communications has been applied to other specific research problems such as designing hotel ranking systems (Ghose et al, 2012), evaluating the influence of reviews on conversion rates (Ludwig et al, 2013) and measuring the impact of reviews on stock performance (Tirunillai and Tellis, 2012).…”

Section: Introductionmentioning

confidence: 99%

The impact of text product reviews on sales

Moon

Park

Kim

2014

European Journal of Marketing

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The aim of this research is to theorize and demonstrate that analyzing consumers' text product reviews using text mining can enhance the explanatory power of a product sales model, particularly for hedonic products, which tend to generate emotional and subjective product evaluations. Previous research in this area has been more focused on utilitarian products. Design/methodology/approach -Our text clustering-based procedure segments text reviews into multiple clusters in association with consumers' numeric ratings to address consumer heterogeneity in taste preferences and quality valuations and the J-distribution of numeric product ratings. This approach is novel in terms of combining text clustering with numeric product ratings to address consumers' subjective product evaluations. Findings -Using the movie industry as our empirical application, we find that our approach of making use of product text reviews can improve the explanatory power and predictive validity of the box-office sales model. Research limitations/implications -Marketing scholars have actively investigated the impact of consumers' online product reviews on product sales, primarily focusing on consumers' numeric product ratings. Recently, studies have also examined user-generated content. Similarly, this study looks into users' textual product reviews to explain product sales. It remains to be seen how generalizable our empirical results are beyond our movie application. Practical implications -Whereas numeric ratings can indicate how much viewers liked products, consumers' reviews can convey why viewers liked or disliked them. Therefore, our review analysis can help marketers understand what factors make new products succeed or fail. Originality/value -Primarily our approach is suitable to products subjectively evaluated, mostly, hedonic products. In doing so, we consider consumer heterogeneity contained in reviews through our review clusters based on their divergent impacts on sales.

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Summarising customer online reviews using a new text mining approach

Cited by 11 publications

References 20 publications

Use of web mining in studying innovation

Use of web mining in studying innovation

A novel concept-level approach for ultra-concise opinion summarization

The impact of text product reviews on sales

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