Scoring products from reviews through application of fuzzy techniques

Ramkumar, V.; Rajasekar, Sakthi Jaya Sundar; Swamynathan, S.

doi:10.1016/j.eswa.2010.03.036

Cited by 20 publications

(6 citation statements)

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“…tourists will only know how good a hotel is, but they do not know the score for hotel features, such as location, service, room and so on. Some researchers (Hu and Liu, 2004;Zhang et al, 2010;Ramkumar et al, 2010;Lau et al, 2014;Ali et al, 2016;Liu et al, 2017;Liu et al, 2019) took it one step further. They are not only able to classify reviews in a binary format, as mentioned above, but are also able to rate product features.…”

Section: Introductionmentioning

confidence: 99%

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FLORA: a hierarchical fuzzy system for online accommodation review analysis

Angskun

2019

JSIT

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Purpose This paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap. Design/methodology/approach FLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine. Findings The performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating. Research limitations/implications This research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base. Practical implications This research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system. Originality/value This research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.

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Section: Introductionmentioning

confidence: 99%

“…They were able to rank products by a feature. Ramkumar et al (2010) scored features of products from online reviews using fuzzy logic. Their algorithm was also able to calculate the spam level of each review.…”

Section: Introductionmentioning

confidence: 99%

FLORA: a hierarchical fuzzy system for online accommodation review analysis

Angskun

2019

JSIT

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“…Jindal and Liu (2008); Lau et al (2011); Mukherjee et al (2012Mukherjee et al ( , 2013), or consider multiple text-based features, using manually identified opinion spam to train classifiers (e.g. Ott et al (2011); Li et al (2011); Ramkumar et al (2010); Fusilier et al (2014)). While these text-based approaches have been used with success, they suffer three major drawbacks (Akoglu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Detection of opinion spam based on anomalous rating deviation

Savage

Zhang

et al. 2015

Expert Systems with Applications

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The publication of fake reviews by parties with vested interests has become a severe problem for consumers who use online product reviews in their decision making. To counter this problem a number of methods for detecting these fake reviews, termed opinion spam, have been proposed. However, to date, many of these methods focus on analysis of review text, making them unsuitable for many review systems where accompanying text is optional, or not possible. Moreover, these approaches are often computationally expensive, requiring extensive resources to handle text analysis over the scale of data typically involved.In this paper, we consider opinion spammers manipulation of average ratings for products, focusing on differences between spammer ratings and the majority opinion of honest reviewers. We propose a lightweight, effective method for detecting opinion spammers based on these differences. This method uses binomial regression to identify reviewers having an anomalous proportion of ratings that deviate from the majority opinion. Experiments on real-world and synthetic data show that our approach is able to successfully identify opinion spammers. Comparison with the current state-of-the-art approach, also based only on ratings, shows that our method is able to achieve similar detection accuracy while removing the need for assumptions regarding probabilities of spam and non-spam reviews and reducing the heavy computation required for learning.

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“…Natural language processing and text mining techniques are employed in the summarization process (Morinaga et al, 2002;Turney, 2002;Hu and Liu, 2004;Nguyen et al, 2007;Ramkumar et al, 2010). Non-textual feature-based approaches evaluate features such as time factor, review length, user reputation and social context.…”

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confidence: 99%

“…Recommending quality book reviews summarize the content of reviews with a clear and brief review summary. Natural language processing and text mining techniques are employed in the summarization process (Morinaga et al, 2002;Turney, 2002;Hu and Liu, 2004;Nguyen et al, 2007;Ramkumar et al, 2010 Jeon et al (2006) proposed a framework to predict the quality of answers for Q&A (question and answer) applications with non-texture features. They suggested that high-quality answers are usually longer than lowquality answers although very long and low-quality answers also exist.…”

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confidence: 99%

Recommending quality book reviews from heterogeneous websites

2013

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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 -In recent years, readers have limited amounts of time to pick the right books to read from a market that is filled with similar types of books. Aiming to read only good books, readers tend to check book reviews written by others. However, it is very difficult to find good book reviews. The aim of this paper is to present a book review recommendation system that collects reviews from heterogeneous sources on the Internet and performs quality judgments automatically. Users can then read the top-ranked reviews suggested by this recommendation system. Design/methodology/approach -In this paper, a book review recommendation system is constructed to collect, process, and judge the quality of book reviews from various heterogeneous sources. The quality measurement of book reviews uses review-evaluation techniques. The prediction results were validated with a ranking list produced by experts. Findings -The proposed system is effective and suitable for recommending quality book reviews from heterogeneous sources. The proposed quality measurement method is more effective than other more commonly used methods. Originality/value -This paper is one of the first to apply review evaluation techniques to the process of book review recommendation. The proposed system can collect and recognize book reviews from different websites with various forms of presentation. This evaluation shows that the quality measurement method produces better results than do other methods, such as ranking by rating score or by the date that the review was posted. Those methods are primarily used by commercial websites.

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Scoring products from reviews through application of fuzzy techniques

Cited by 20 publications

References 4 publications

FLORA: a hierarchical fuzzy system for online accommodation review analysis

FLORA: a hierarchical fuzzy system for online accommodation review analysis

Detection of opinion spam based on anomalous rating deviation

Recommending quality book reviews from heterogeneous websites

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