Feature Extraction and Opinion Mining in Online Product Reviews

Aravindan, Siddharth; Ekbal, Asif

doi:10.1109/icit.2014.72

Cited by 16 publications

(6 citation statements)

References 9 publications

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“…Finally, their method was shown to be more accurate than existing methods in terms of pre-Information Technology and Control 2023/3/52 620 diction. Aravindan and Ekbal [7] extracted the most relevant features from review comments as positive and negative. They have performed association rule mining over the text and also considered the polarity value of the terms.…”

Section: Related Workmentioning

confidence: 99%

A New Sentiment and Fuzzy Aware Product Recommendation System Using Weighted Aquila Optimization and GRNN in e-Commerce

Rosewelt,

Raju,

Sujatha

2023

ITC

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Customer reviews are playing an important role in e-commerce for increasing sales by knowing the customer’s purchase pattern and expectations. The reviews that are collected after completing their purchase reflect the quality and services in e-commerce. The user’s reviews are characterized and categorized through sentiment and semantic analysis. Moreover, the sentiment and semantic classification processes are also performed to predict the user’s purchase patterns and liked products. However, the available classification is not able to predict the user’s purchase patterns. In this paper, we propose a new Product Recommendation System (PRS) to predict the appropriate product for users based on their purchase behavior and pattern. The proposed recommendation system incorporates the standard data preprocessing tasks like tokenization process, Parts of Speech (PoS) tagging process, and parsing, a new sentiment and semantic score calculation procedure, and a new feature optimization technique called the Weighted Aquila Optimization Method (WAOM). Moreover, the sentiment and semantic classification processes are performed by applying a General Regression Neural Network with the incorporation of fuzzy temporal features (FTGRNN) and obtaining better classification results. The newly developed PRS is evaluated by conducting experiments in this work and also proved as superior than other systems available in this direction in terms of prediction accuracy, precision, recall, serendipity and nDCG.

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Section: Related Workmentioning

confidence: 99%

A New Sentiment and Fuzzy Aware Product Recommendation System Using Weighted Aquila Optimization and GRNN in e-Commerce

Rosewelt,

Raju,

Sujatha

2023

ITC

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“…(iv) The existing framework has given one component that anybody can give input about any item. The individual from testing E-shopping site can give counterfeit criticism to the first site [13,21].…”

Section: Motivation For Studymentioning

confidence: 99%

Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering

et al. 2018

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Sites for web-based shopping are winding up increasingly famous these days. Organizations are anxious to think about their client purchasing conduct to build their item deal. Internet shopping is a method for powerful exchange among cash and merchandise which is finished by end clients without investing a huge energy spam. The goal of this paper is to dissect the high-recommendation web-based business sites with the help of a collection strategy and a swarm-based improvement system. At first, the client surveys of the items from web-based business locales with a few features were gathered and, afterward, a fuzzy c-means (FCM) grouping strategy to group the features for a less demanding procedure was utilized. Also, the novelty of this work—the Dragonfly Algorithm (DA)—recognizes ideal features of the items in sites, and an advanced ideal feature-based positioning procedure will be directed to discover, at long last, which web-based business webpage is best and easy to understand. From the execution, the outcomes demonstrate the greatest exactness rate, that is, 94.56% compared with existing methods.

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“…Further for these terms weights was concluded followed by classification. Aravindan & Ekbal (2014) uses a combination of association mining and support vector machine, along with part of speech tagging, sentiment computation, feature estimation using plus three minus three vicinities amongst words is deployed to perform feature extraction and opinion classification for them. Extraction and summarization of feature are done using both probabilistic and association mining models in Bafna & Toshniwal (2013).…”

Section: Feature Extraction and Text Summarizationmentioning

confidence: 99%

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

Anand

Srivastava

Rakshit

2017

PAJAIS

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The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work.

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Feature Extraction and Opinion Mining in Online Product Reviews

Cited by 16 publications

References 9 publications

A New Sentiment and Fuzzy Aware Product Recommendation System Using Weighted Aquila Optimization and GRNN in e-Commerce

A New Sentiment and Fuzzy Aware Product Recommendation System Using Weighted Aquila Optimization and GRNN in e-Commerce

Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

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