Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Competitive analysis of online reviews using exploratory text mining

Purpose – This paper explores the usefulness of analyzing text-based online reviews using text mining tools and visual analytics for SWOT Analysis, as applied to the hotel industry. These results can be used to develop competitive actions. Design – The text mining/visualization tool, ReviewMap, was used to transform an archive of reviews spanning multiple suppliers into a hierarchy of data of increasing dimensionality. Visual summaries at each level were integrated to propagate selections at one level throughout the rest of the hierarchy. These visual summaries identify features required for competition at a given level and features that currently discriminate amongst competitors. Methodology – The approach was exploratory, the objective of which was to determine if useable competitive intelligence could be found in a typical collection of online reviews for a set of competing hotels. A publically available collection of reviews was subjected to a set of text mining procedures and visual analyses in order to summarize the features and opinions expressed. Originality – Prior analyses of online reviews relied solely upon numeric “star” ratings. This study utilized text mining to uncover information within the written comments and applied the information in a SWOT Analysis of three competing hotels. Findings – In the set of reviews used in this paper, a common measure of analytical power almost doubled when text mining summaries of the written comments were used in combination with numeric ratings. Visual analytics revealed the dominant features for each hotel, the features required of all hotels competing at a given level, and the features that define specific positions within the competitive landscape. This analysis of strengths, weaknesses, opportunities and threats revealed several promising competitive actions for the hotels in the study

Hotel online reviews: different languages, different opinions

Online reviews are one of the main influencers of hotel purchase decisions. This study performs an analysis of reviews extracted from well-known online review sources in combination with hotel sales data and concludes that ratings differ according to the language of reviews. Data science tools have been applied to English, Spanish, and Portuguese reviews, revealing that reviews written in English achieve higher ratings when compared with Spanish or Portuguese reviews. A new visualization method is proposed to quickly depict the sentiment of main topics mentioned in reviews, clearly revealing that not all customers are influenced by reviews in the same way or look for the same things in a hotel. This study has great implications for online reviews research and for hotel management as it clearly shows that language can be used to identify preferences of guests from different origins and because it gives hoteliers more information on how to provide a better service according to guests’ cultural background.info:eu-repo/semantics/acceptedVersio