Conference Paper

Aspect and sentiment unification model for online review analysis

DOI: 10.1145/1935826.1935932 Conference: Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM 2011, Hong Kong, China, February 9-12, 2011
Source: DBLP


User-generated reviews on the Web contain sentiments about detailed aspects of products and services. However, most of the reviews are plain text and thus require much effort to obtain information about relevant details. In this paper, we tackle the problem of automatically discovering what aspects are evaluated in reviews and how sentiments for different aspects are expressed. We first propose Sentence-LDA (SLDA), a probabilistic generative model that assumes all words in a single sentence are generated from one aspect. We then extend SLDA to Aspect and Sentiment Unification Model (ASUM), which incorporates aspect and sentiment together to model sentiments toward different aspects. ASUM discovers pairs of {aspect, sentiment} which we call senti-aspects. We applied SLDA and ASUM to reviews of electronic devices and restaurants. The results show that the aspects discovered by SLDA match evaluative details of the reviews, and the senti-aspects found by ASUM capture important aspects that are closely coupled with a sentiment. The results of sentiment classification show that ASUM outperforms other generative models and comes close to supervised classification methods. One important advantage of ASUM is that it does not require any sentiment labels of the reviews, which are often expensive to obtain.

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Available from: Alice Oh, Oct 31, 2014
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    • "Food, service and atmospherics Jo and Oh (2011) Types of cuisine and food Soriano (2002) Quality of the food, cost/value of the meal, quality of the service and place Ha and Jang (2010) Atmospherics, food and service Kim, Lee, and Yoo (2006) Physical environment, food quality, customer orientation, relationship benefits and price fairness Haghighi, Dorosti, Rahnama, and Hoseinpour (2012) Food quality, price, service quality, location and atmosphere Haghighi et al. (2012) Cleanliness, atmosphere, space, convenient hours, food quality, staff behaviour, price and responsiveness Pantelidis (2010) Food, service, atmosphere, price, menu and design From the table presented above, we can observe that the most talked-about attribute was food. With this consideration in mind, we establish a model considering different food dimensions found in the literature. "
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    Procedia - Social and Behavioral Sciences 10/2015; 175:162-169. DOI:10.1016/j.sbspro.2015.01.1187
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    • "θ djk = n d,j,k,·,· + α n d,j,·,·,· + K * α (1) φ jksw = n ·,j,k,s,w + β w s V w=1 n ·,j,k,s,w + ˆ βs (2) ξ djks = n d,j,k,s,· + γ n d,j,k,·,· + 3 * γ (3) 3.1.2 USTM-FT(S) Previous studies [8] [10] indicate that assigning sentiment to the entire sentence rather than each individual word might be a better strategy for opinion mining. Following this idea, we propose USTM-FT(S), which is based on the assumption that all the words in a given sentence have the same sentiment, but can be associated with different tags and topics. "
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    ABSTRACT: The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many on-line consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in sentiment analysis has largely ignored these additional user meta-data. To address this deficiency, in this paper, we propose parametric and non-parametric User-aware Sentiment Topic Models (USTM) that incorporate demographic information of review authors into topic modeling process in order to discover associations between market segments, topical aspects and sentiments. Qualitative examination of the topics discovered using USTM framework in the two datasets collected from popular online consumer review platforms as well as quantitative evaluation of the methods utilizing those topics for the tasks of review sentiment classification and user attribute prediction both indicate the utility of accounting for demographic information of review authors in opinion mining.
    38th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'15); 08/2015
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    • "An important part of such supportive work is the identification of the relevant aspects or facets of the topic (e.g., the ambience of a restaurant vs. its food or staff or cleanliness) and the correspondent sentiment; see (Brody and Elhadad, 2010; Lu et al., 2011; Titov and McDonald, 2008; Jo and Oh, 2011; Xueke et al., 2013; Kim et al., 2013; García- Moya et al., 2013; Wang et al., 2011; Moghaddam and Ester, 2012). Online reviews (about products or offerings) in crowdsourcing and traditional sites (e.g., yelp, Amazon, Consumer Reports) include some sort of aspect-oriented star rating systems where more stars indicate higher level of satisfaction . "
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    ABSTRACT: In this paper, we described possible directions for deeper understanding, helping bridge the gap between psychology / cognitive science and computational approaches in sentiment/opinion analysis literature. We focus on the opinion holder's underlying needs and their resultant goals, which, in a utilitarian model of sentiment, provides the basis for explaining the reason a sentiment valence is held. While these thoughts are still immature, scattered, unstructured, and even imaginary, we believe that these perspectives might suggest fruitful avenues for various kinds of future work.
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