Aspect Mining: Extracting Important Aspects From Consumers- Review

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 11, 2016 | ISSN (online): 2321-0613

Aspect Mining: Extracting Important Aspects from Consumers’ Review Mr. Dhondu Wathore1 Mr. Manish Rane2 Mr. Shailesh Kamble3 Mr. Riyaj Pathan4 1,2,3,4 Department of Computer Engineering 1,2,3,4 STES Smt. Kashibai Navale College of Engineering Abstract— Thousands of aspects are associated with product. Some of these aspects are important while some are not. Important aspects influence the purchasing decision of the consumer. Hence indentifying the important aspects becomes necessary for consumers as well as for manufacturer. Consumer can buy good product as per their requirement based on the aspect information and manufactures can improve the quality of their product and can focus on improving the quality of poor aspects. This paper describes various techniques used for important aspect identification and aspect ranking.

Key words: Aspect, Aspect Identification, Aspect Ranking, Consumer Review, E-Commerce, Product Aspect, Sentimental Analysis I. INTRODUCTION There are various e-commerce sites available which ask consumers to give reviews about the product. Also there are various forums such as cnet.com which contains reviews about the product. A review may be like ‘sound quality of Dolby speaker is amazing ’. This review reveals positive opinion about the aspect ‘sound quality’ of product ‘Dolby speaker’. Thus reviews contain valuable information about the aspect of product which can be used by the customers to make purchasing decision, also by manufactures to improve quality of their product and to implement marketing strategy.

But generally product has numerous aspect and only some of them influence the customers’ purchasing decision. Hence identification of important aspects is important. But manual Identification of important aspects from reviews is not possible as thousands of reviews available. Hence automatic Identification of important aspects is necessary so as to save the time and resources of consumers and firms.

In this paper we present the techniques for identifying the important aspects, classification of aspects and ranking of those aspects.

II. RELATED WORK Product Aspect Ranking involves aspect identification and sentiment analysis.

A. Approaches for Aspect Identification 1) Supervised: This involves building of extractor which extracts aspects from reviews. This requires labeled training dataset. Wong and Lam used the HMM model and conditional random field to learn the extractor. Li et al used skip CRF and tree CRF to learn the extractor [3].

2) Unsupervised: This approach doesn’t require the training dataset like supervised approach. It automatically identifies the aspects from reviews. Wu et al uses a phrase dependency parsing.

Popesu and Etzioni developed OPINE system for extracting

aspects from reviews. Su et al design a reinforcement strategy [6].

B. Approaches for Sentiment Classification 1) Lexicon Based Approach: Lexicon consists of all opinion words, phrases, idioms. Hu and Liu use this method [7].

2) Holistic Based Approach: Ding et al used a Holistic lexicon based method. This method addresses the issue that the opinion of sentiment words would be content sensitive.

3) Supervised Learning Approach: In this Naïve Bayes classifier, Maximum Entropy classifier, Support Vector Machines are models which can be used for sentiment analysis.

III. PRODUCT ASPECT RANKING TECHNIQUE Input to the system is reviews, which is collected from sources like e-commerce site, forums etc. Product aspect ranking involves following major steps.  Identifying the Important Aspects  Sentimental analysis of reviews associated with the aspect.  Ranking of aspects based on the aspect frequency and opinions associated with them A. Identifying the Important Aspects Input: Pros and cons reviews and free text reviews Output: Product aspects For pros and cons reviews only aspect identification is required. For that frequent nouns or noun phrases are extracted. For identifying aspects in free text review, these reviews are split into sentences and parsed using parser. Parser gives the parse tree as the output from which noun phrases are to be extracted. One class Naïve Bayes classifier is learnt using the aspects from pros and cons review and from the all the known aspects. This classifier is then used to identify the aspects from free text reviews. Identified aspect may contain the synonym term which hence synonym clustering is done to obtain unique aspects. Synonym dictionary is used to collect synonym term.

B. Sentimental Analysis of Reviews Associated with the aspect Opinion Orientation about aspects is already present in pros and cons review. This pros and cons are used to build a sentimental classifier. Pros and cons review contain valuable information for learning a sentimental classifier, which is then used for analysis of free text review. First sentiment terms in pros and cons review are collected then these sentiment terms are used to train the sentiment classifier.

C. Ranking of Product Aspects After finding aspect and opinions associated with them ranking of aspect is done. For ranking the aspects, weight of

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Aspect Mining: Extracting Important Aspects from Consumers’ Review (IJSRD/Vol. 3/Issue 11/2016/074)

the aspect is calculated. For calculation of weight, value of aspect is necessary along with occurrence frequency of positively and negatively opinionated word. TFIDF is commonly used for calculation of weight of term in document. Here same concept is applied for calculation of value of aspect term. Finally Weight of aspect is calculated by using aspect value given by TFIDF and occurrence frequency of positively opinionated word and negatively opinionated words associated with aspect term.

IV. APPLICATIONS Product aspect ranking is applicable to wide range of real world application. For example it can be used to summarize the reviews for a particular product which can then use by firms to analyze the feedback. This kind of review summarization can be seen in Android Play store. It can used to make comparison of product belonging to same category but manufactured by different vendors. Based on the aspect ranking technique, Ranking of the Product itself can be done considering the specific aspect.

V. CONCLUSION Reviews about product from various manufacture helps the consumers to search for product as per their requirement and buy good quality product. With the increase in use of ecommerce sites numbers of reviews are available about numerous products which contain the valuable information.

Hence to help the customer to easily find the right product and to help manufactures to analyze the feedback of their product extraction of information is necessary which can be achieved by opinion based aspect mining.

REFERENCES [1] Deepika Lokhande, Khairnar Rohini, Mahale Pooja, “Aspect Extraction Ranking of Product for Online Reviews”, IJCA, 2015.

[2] Zheng-JunZha, Jianxing Yu, Jinhui Tang, Meng Wang, and Tat-Seng Chua, “Product Aspect Ranking and Its Applications”, IEEE TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING, vol.26, no.5, May 2014.

[3] Rutuja Tikait, Ranjana Badre and Mayura Kinikar, “Product Aspect Ranking Techniques: A Survey”, IJIRCCE, vol.2, issue 11, Nov 2014.

[4] Richa Sharma, Shweta Nigam, Rekha Jain, “MINING OF PRODUCT REVIEWS AT ASPECT LEVEL”, IJFCST, Vol.4, No.3, May 2014.

[5] Justin Martineau, and Tim Finin, “Delta TFIDF: An Improved Feature Space for Sentiment Analysis”, Proceedings of the Third International ICWSM Conference, 2009.

[6] A. M. Popescu and O. Etzioni, “Extracting Product Features and Opinions from Reviews”, in Proc.

HLT/EMNLP, Vancouver, BC, Canada, pp. 339–346, 2005.

[7] M. Hu and B. Liu, “Mining and Summarizing Customer Reviews,” in Proc. SIGKDD, Seattle, WA, USA, pp.

168–177, 2004.

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