Mobile Speech and Advanced Natural Language Solutions || Summarizing Opinion-Related Information for Mobile Devices

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  • 289A. Neustein and J.A. Markowitz (eds.), Mobile Speech and Advanced Natural Language Solutions, DOI 10.1007/978-1-4614-6018-3_11, Springer Science+Business Media New York 2013

    Abstract Reviews about products and services are abundantly available online. However, gathering information relevant to shoppers involves a signi fi cant amount of time reading reviews and weeding out extraneous information. While recent work in multi-document summarization has attempted to some degree to address this challenge, many questions about extracting and aggregating opinions remain unan-swered. This chapter demonstrates a novel approach to review summarization, using three techniques: (1) graphical summarization; (2) review summarization; and (3) a hybrid approach, which combines abstractive and extractive summarization meth-ods, to extract relevant opinions and relative ratings from text documents. All three methods allow a consistent approach to preserve the overall opinion distribution that is expressed in the original reviews.

    G. Di Fabbrizio (*) Lead Member of Technical Staff , AT&T Labs Research , 180 Park Avenue Building 103 , Florham Park , NJ , USA e-mail:

    A. J. Stent Principal Member of Technical Staff , AT&T Labs Research , 180 Park Avenue Building 103 , Florham Park , NJ 07932 , USA e-mail:

    R. Gaizauskas Department of Computer Science , University of Shef fi eld, Regent Court , 211 Portobello Street , Shef fi eld , S1 4DP , UK e-mail: R.Gaizauskas@shef fi

    Chapter 11 Summarizing Opinion-Related Information for Mobile Devices

    Giuseppe Di Fabbrizio , Amanda J. Stent , and Robert Gaizauskas

  • 290 G. Di Fabbrizio et al.


    The last 5 years have seen transformational advances in the use of advanced networked mobile devices, enabling users to merge their online and of fl ine lives as never before. One of the daily life tasks that is most illustrative of this transforma-tion is purchasing . Consumers on-the-go increasingly rely on internet search to fi nd services and products, and on online reviews to select from among them. The actual purchase of the selected service or product may take place online or at a bricks-and-mortar location. A study conducted by The E-tailing Group 1 describes an emerg-ing breed of shopper, the social researcher , who seeks out opinions expressed by online peers before making buying decisions. According to this research, 78 % of the 1,200 sampled consumers spent more than 10 min reading reviews online. Additionally, 65 % meet the de fi nition of social researchers and 86 % of social researchers rated online reviews and product ratings an extremely or very important factor in fl uencing their buying decisions. Another study 2 carried out by comScore and The Kelsey Group revealed that a signi fi cant portion of of fl ine product and service sales (24 % of the 2,000 interviewed users) are made after consulting online reviews while three quarters of consumers who consulted online reviews reported that the reviews had a signi fi cant in fl uence on their purchase. Retailers and service providers are also recognizing that there is a growing crowd of shoppers who rely on reviews to learn about products and services and, ultimately, make decisions about spending their money (Chevalier and Mayzlin 2006 ; Duan et al. 2008 ; Park et al. 2007 ) .

    Reviews about products and services are abundantly available online. However, even from traditional PC interfaces, identifying relevant information in product and service search results involves a signi fi cant amount of time reading reviews and weeding out extraneous information. The mobile device presents new challenges and opportunities to developers of both search and review browsing services:

    Screen size Mobile devices have relatively small displays and limited naviga- tion capabilities. Requested information should be presented in only one to two screens to minimize vertical and horizontal scrolling. This presents opportunities for intelligent pruning, grouping, and summarization of search results and reviews. Time Mobile users are often on-the-move with limited time to re fi ne search criteria or select relevant information from a long list of results. Since time is of the essence, the presented information should be targeted to the users speci fi c goal. Location Mobile users are highly focused on executing geographically local plans such as fi nding restaurants, entertainment events, or retail stores. The precision

    1 fi nes-new-breed-of-shopper-the-social-researcher-2347 . 2 .

  • 29111 Summarizing Opinion-Related Information for Mobile Devices

    of presented information can be improved by considering the users location and nearby businesses. For example, a system may choose to only present search results within walking or driving distance of the user. Personalization For mobile users, personal data (e.g., search and purchasing histories) can be used to improve the precision of search results and the informa-tiveness of reviews.

    Although constrained by the same factors, typically, mobile search and mobile review browsing are treated as different tasks using a combination of poorly inte-grated algorithms. This leads to inef fi ciencies and decreases user satisfaction.

    For example, imagine that a consumer wants to buy Skechers shoes. The con-sumer would fi rst use a local mobile search engine to fi nd nearby shoe stores (see Fig. 11.1 ). The search engine might re-rank search results by using geographic information about the current users location (Stent et al. 2009 ) or an explicitly requested location and, optionally, re-score the fi nal results based on domain knowledge and/or the users search history. Once in the store, the user may use a separate internet search to fi nd and browse online reviews and ratings for particular types of shoes. Opinion mining and sentiment analysis methods can be applied to extract the targets, and the relative polarity (e.g., positive, negative, or neutral), of the opinions expressed in the reviews (Hu and Liu 2004 ; McDonald et al. 2007 ; Pang and Lee 2008 ) . Lastly, the user must synthesize (or summarize ) all the facts, opin-ions, and ratings read in the previous step to fi nd the most desirable option.

    While there exist relatively accurate and ef fi cient methods for the two steps in this process (search, and sentiment analysis), summarization of evaluative text (e.g., documents containing opinion or sentiment-laden text) is a fairly new

    Fig. 11.1 Spek4it: local business search by voice

  • 292 G. Di Fabbrizio et al.

    technology. Yet it is important for mobile users, due to the small amount of screen real estate they have access to and the distractions of other tasks they may be performing.

    In this chapter, we describe three examples of product and service review sum-marization techniques. The fi rst approach is a graphical summarization method that predicts the number of stars (the polarity and strength of opinion) a reviewer may have assigned to a speci fi c topic based on the opinion expressed in the textual review. In this scenario, a set of prede fi ned topics are ranked on a scale from one (poor) to fi ve (excellent) stars and graphically visualized on a mobile device. The second method describes a review summarization technique based on natural lan-guage generation where review ratings and other review features are used to auto-matically generate a short natural language description of the opinions expressed across the reviews. The last proposed technique is a hybrid approach that combines abstractive and extractive summarization methods and interleaves quotes from reviewers directly into the natural language summary.

    The rest of the chapter is structured as follows: section What Is in a Review? describes the main characteristics of product and service reviews. Section Case Study in Mobile Search and Reviewing: Have2eat presents a case study in mobile searching and reviewing. section Sentiment Analysis Using Multi-rating Multi-aspect Predictions illustrates sentiment analysis methods to predict review ratings. section Review Summarization shows text summarization approaches to review synthesis. section Conclusions concludes and presents future work.

    What Is in a Review?

    What makes reviews different from other user-generated content such as blogs, wikis, social network documents, or message boards? Reviews are usually either about a single product , e.g., consumer goods including digital cameras, DVD play-ers, or books; or related to a service like lodging in an hotel or dining in a restaurant. Typically a product or service has several ratable aspects (sometimes referred also as topics or features ). This means that a review can be viewed as a set of aspects, each with an associated rating . Ratings de fi ne the strength and polarity of opinions and typically range over integer values; they often visualized with star symbols.

    Yelp , 3 for example, combines local business reviews and social networking capabilities to rank businesses. Figure 11.2 shows the page dedicated to the Japanese restaurant Santouka Ramen located in Los Angeles, CA. The business has been reviewed by 944 customers, each of whom wrote a textual descriptions of their experience the review and rated their overall experience with a star-based score the overall rating from one (poor) to fi ve (excellent).

    3 .

  • 29311 Summarizing Opinion-Related Information for Mobile Devices

    The rating distribution graph visualized in the Reviews Highlights section of Fig. 11.2 presents a half-bell shaped curve indicting that most reviewers opinions about this business are biased towards the positive end of the ratings. But a better understanding of the opinions expressed by reviewers would require decomposing the overall ratings based on aspects that are relevant to a dining experience. Each contributing aspect would be associated with a more detailed rating and with rele-vant parts of the reviewers textual description. This would allow a reader to easily identify whether users most appreciated the good service, the quality of the special of the day or the dcor. With only overall ratings, fi nding these fi ne-grained details requires skimming through most of the 944 reviews, some of them very lengthy and with substantial amounts of extraneous or irrelevant information. Thus, the abun-dance of online products and service reviews does not optimally help users, who must search and skim through thousands of documents to identify the information relevant to them.

    These considerations suggest that there is a substantial need for automatic meth-ods to summarize the content of reviews. This is particularly true for mobile users, who are constrained by small displays. However, traditional extractive document and multi-document summarization techniques, where relevant fragments of text are selected from input documents and concatenated into a consistent summary

    Fig. 11.2 Yelps Web browser- and mobile-based visualization of reviews for the Santouka Ramen restaurant in Los Angeles, CA

  • 294 G. Di Fabbrizio et al.

    (Goldstein et al. 2000 ) , are not helpful for evaluative texts covering multiple aspects of an entity, with a range of opinions for each aspect. Without a robust semantic model of entities, their aspects, and opinion strengths and polarities, automatic extractive summarizers produce incoherent summaries. In other words, automatic summarization of evaluative texts requires two components: sentiment analysis and extraction, and then summarization.

    As pointed out by Wiebe et al. ( 2004 ) , evaluative language presents speci fi c lin-guistic characteristics that are usually missed in traditional natural language pro-cessing approaches. Sentiment analysis techniques for evaluative texts must identify the linguistic elements realizing sentiment, their target domain-relevant aspects, and their semantic orientation in the context of the document. These elements are often lexical , ranging from single words (e.g., fantastic, dreadful, good ) to more complex syntax structures (e.g., to put it mildly, stand in awe , the most disap-pointing place that I have stayed ). Wiebe et al. ( 2004 ) refer to three types of senti-ment clues: (1) hapax legomena unique words appearing only once in the text; (2) collocations word ngrams frequently occurring in subjective sentences; and (3) adjectives and verbs extracted by clustering according to a distribution similarity criterion (Lin 1998 ) . Additionally, the contexts where the clues appear in the sen-tences play a key role in determining actual polarity of the opinions being expressed. Polanyi and Zaenen ( 2005 ) describe additional clues contextual value shifters that modify the positive or negative contributions of other clues. Consider the posi-tive word ef fi cient ; when modi fi ed by the intensi fi er rather , the resulting rather ef fi cient is a less strongly positive expression.

    One fi nal consideration for review analysis and summarization is authenticity. Fake, or spammy reviews are increasingly common. Detecting fake reviews is very hard; recent contributions (Feng et al. 2012 ) show some promising techniques to fi nd deceptive product reviews by analyzing sudden changes in the rating distribu-tion footprints. The likely presence of fake reviews in any dataset makes it even more important that review summaries accurately capture the full range of opinions expressed.

    Case Study in Mobile Search and Reviewing: Have2eat

    Have2eat 4 is a popular restaurant search and reviewing application available for iPhone and Android-based devices. During search, Have2eat uses geo-location information (from the GPS device or explicitly entered by the user) to produce a list of matching restaurants sorted by distance and located within a speci fi c radius from the originating location. During browsing of search results, when restaurant reviews

    4 .

  • 29511 Summarizing Opinion-Related Information for Mobile Devices

    are available, a compact one-screen digest displays a summary of the reviews posted on the web by other customers. Customers can expand to read a full review page and also enter their own ratings, comments and feedback. The review summaries are visualized on the mobile screen:

    Graphically by thumbs-up (positive reviews) and thumbs-down (negative reviews) for different aspects of the restaurant; Textually by a few sent...


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