Product comparison using comparative relations

Si, Li; Zha, Zheng-Jun; Ming, Zhaoyan; Wang, Meng; Chua, Tat-Seng; Guo, Jun; Wang, Xu

doi:10.1145/2009916.2010094

Cited by 50 publications

(35 citation statements)

References 5 publications

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“…In Sect. 5.2, it can be seen that comparative opinions can be used to model products' comparative relationships via a graph, and thus improve the products' ranking quality (Jamroonsilp and Prompoon 2013;Li et al 2011;Zhang et al 2010). 7. Review emotions: Emotion reflects a reviewer's mood (e.g., sadness, joy, distress, happiness, etc.)…”

Section: Review Elementsmentioning

confidence: 99%

“…Another related study uses product-related community-based Question Answering (cQA) pairs as an additional source of information about comparative opinions (Li et al 2011). For example, the phrase "I'd go with" can accommodate a preferred product in the answer.…”

Section: Considering Comparative Opinionsmentioning

confidence: 99%

“…Zhang et al (2010) conduct their experiment on a dataset of 1,350 digital cameras (with 83,005 reviews) and 760 televisions (with 24,495 reviews), collected from Amazon. Li et al (2011) perform a user study (involving 12 ordinary users) to test their approach. Their two datasets include, respectively, 50,893 reviews of six mobile phones and 3,604 reviews of five MP3 players, collected from multiple websites such as Cnet, Amazon, Reevoo, and Gsmarena.…”

Section: Considering Comparative Opinionsmentioning

confidence: 99%

“…In the second sub-category, comparative opinions are used to establish a comparison relationship graph among products. The related studies primarily differ in terms of the algorithm developed to process the graph, which determines each product's superiority score (Jamroonsilp and Prompoon 2013;Li et al 2011) or performs the ranking directly . The algorithm's outcome is validated by human experts or ordinary users.…”

Section: Considering Comparative Opinionsmentioning

confidence: 99%

See 3 more Smart Citations

Recommender systems based on user reviews: the state of the art

Chen

Wang

2015

User Model User-Adap Inter

312

187

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In recent years, a variety of review-based recommender systems have been developed, with the goal of incorporating the valuable information in user-generated textual reviews into the user modeling and recommending process. Advanced text analysis and opinion mining techniques enable the extraction of various types of review elements, such as the discussed topics, the multi-faceted nature of opinions, contextual information, comparative opinions, and reviewers' emotions. In this article, we provide a comprehensive overview of how the review elements have been exploited to improve standard content-based recommending, collaborative filtering, and preference-based product ranking techniques. The review-based recommender system's ability to alleviate the well-known rating sparsity and cold-start problems is emphasized. This survey classifies state-of-the-art studies into two principal branches: review-based user profile building and review-based product profile building. In the user profile sub-branch, the reviews are not only used to create term-based profiles, but also to infer or enhance ratings. Multi-faceted opinions can further be exploited to derive the weight/value preferences that users place on particular features. In another sub-branch, the product profile can be enriched with feature opinions or comparative opinions to better reflect its assessment quality. The merit of each branch of work is discussed in terms of both algorithm development and the way in which the proposed

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Section: Review Elementsmentioning

confidence: 99%

Section: Considering Comparative Opinionsmentioning

confidence: 99%

Section: Considering Comparative Opinionsmentioning

confidence: 99%

Section: Considering Comparative Opinionsmentioning

confidence: 99%

See 2 more Smart Citations

Recommender systems based on user reviews: the state of the art

Chen

Wang

2015

User Model User-Adap Inter

312

187

View full text Add to dashboard Cite

show abstract

“…Others are interested in yet another task to identify the direction of the comparisons (Ganapathibhotla and Liu, 2008;Tkachenko and Lauw, 2014), or the aggregated ranking (Kurashima et al, 2008;Zhang et al, 2013;Li et al, 2011). Our task precedes these tasks in the pipeline.…”

Section: Related Workmentioning

confidence: 99%

A Convolution Kernel Approach to Identifying Comparisons in Text

Tkachenko

Lauw

2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

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Comparisons in text, such as in online reviews, serve as useful decision aids. In this paper, we focus on the task of identifying whether a comparison exists between a specific pair of entity mentions in a sentence. This formulation is transformative, as previous work only seeks to determine whether a sentence is comparative, which is presumptuous in the event the sentence mentions multiple entities and is comparing only some, not all, of them. Our approach leverages not only lexical features such as salient words, but also structural features expressing the relationships among words and entity mentions. To model these features seamlessly, we rely on a dependency tree representation, and investigate the applicability of a series of tree kernels. This leads to the development of a new context-sensitive tree kernel: Skip-node Kernel (SNK). We further describe both its exact and approximate computations. Through experiments on real-life datasets, we evaluate the effectiveness of our kernel-based approach for comparison identification, as well as the utility of SNK and its approximations.

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Comparative opinion mining: A review

Varathan

Giachanou

Crestani

2016

Asso for Info Science & Tech

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Opinion mining refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyse the public opinion on a number of different topics. Comparative opinion mining is a subfield of opinion mining that deals with identifying and extracting information that is expressed in a comparative form (e.g. "paper X is better than the Y").Comparative opinion mining plays a very important role when ones tries to evaluate something, as it provides a reference point for the comparison. This paper provides a review of the area of comparative opinion mining. It is the first review that cover specifically this topic as all previous reviews dealt mostly with general opinion mining. This survey covers comparative opinion mining from two different angles.One from perspective of techniques and the other from perspective of comparative opinion elements. It also incorporates preprocessing tools as well as dataset that were used by the past researchers that can be useful to the future researchers in the field of comparative opinion mining.

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Product comparison using comparative relations

Cited by 50 publications

References 5 publications

Recommender systems based on user reviews: the state of the art

Recommender systems based on user reviews: the state of the art

A Convolution Kernel Approach to Identifying Comparisons in Text

Comparative opinion mining: A review

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