scholarly journals Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach

2022 ◽  
Vol 3 (4) ◽  
pp. 283-294
Author(s):  
M. Duraipandian ◽  
R. Vinothkanna
Keyword(s):  
Opinion Mining ◽  
Online Reviews ◽  
Classification Model ◽  
Product Reviews ◽  
Consumer Evaluations ◽  
Proposed Model ◽  
Svm Model ◽  
Review Helpfulness ◽  
Hybrid Classification ◽  
Corporate Trust

Customers post online product reviews based on their own experience. They may share their thoughts and comments on items on online shopping websites. The sentiment analysis comprises of opinion or idea process and process of sorting high rating reviews according to how well the product satisfies. Opinion mining is a technique for extracting useful data from large amounts of texts in order to use those to enhance or expand a company's operations. According to consumer evaluations, many of the goods aren't as good as they seem. It's common that buyers submit their thoughts on a product but then forget to rate it. The prior data preprocessing is more efficient to extract the features by CNN approach. This proposed methodology breaks down each user's rating prediction model into two parts: one based on the review text and other based on the user rating matrix with the help of CNN feature engineering. The goal of this study is to classify all reviews into ratings by SVM model. This proposed classification model provides good accuracy to predict the online reviews efficiently. For reviews without ratings, a further prediction of feelings is generated using multiple classifiers. The benefits of this proposed model are honed using helpfulness ratings from a small number of evaluations such as accuracy, F1 score, sensitivity, and precision. According to studies using the standard benchmark dataset, the accuracy of customized recommendation services, user happiness, and corporate trust may all be enhanced by including review helpfulness information in the recommender system.

scholarly journals Aspect Βased Classification Model for Social Reviews

2017 ◽  
Vol 7 (6) ◽  
pp. 2296-2302 ◽  
Author(s):  
J. Mir ◽  
A. Mahmood ◽  
S. Khatoon
Keyword(s):  
Opinion Mining ◽  
Naive Bayes ◽  
Word Identification ◽  
Naïve Bayes ◽  
Classification Model ◽  
Product Reviews ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Proposed Model

Aspect based opinion mining investigates deeply, the emotions related to one’s aspects. Aspects and opinion word identification is the core task of aspect based opinion mining. In previous studies aspect based opinion mining have been applied on service or product domain. Moreover, product reviews are short and simple whereas, social reviews are long and complex. However, this study introduces an efficient model for social reviews which classifies aspects and opinion words related to social domain. The main contributions of this paper are auto tagging and data training phase, feature set definition and dictionary usage. Proposed model results are compared with CR model and Naïve Bayes classifier on same dataset having accuracy 98.17% and precision 96.01%, while recall and F1 are 96.00% and 96.01% respectively. The experimental results show that the proposed model performs better than the CR model and Naïve Bayes classifier.

Product Quality Assessment Based on Online Reviews

2019 ◽  
Vol 11 (3) ◽  
pp. 81-97
Author(s):  
Chao Li ◽  
Jun Xiang ◽  
Shiqiang Chen
Keyword(s):  
Quality Assessment ◽  
Product Quality ◽  
Opinion Mining ◽  
Online Reviews ◽  
Product Reviews ◽  
Product Features ◽  
Related Quality ◽  
Quality Dimensions

Reviews can reflect the degree of consumers' satisfaction and views on product quality, and consumers tend to read product reviews and then get helpful information about product quality before placing an order in e-commerce platforms. However, the existing research mainly focus on the assessment of review quality, fake review detection, opinion mining, and there is little research to assess product quality from the perspectives of product features based on reviews objectively and quantifialy. Therefore, the authors propose a method to assess product quality based on reviews in a granularity of product feature. The authors define the related quality dimensions and develop the corresponding assessment models, assess the review quality crawled from an e-commerce platform, then extract product features and opinion words from the quality reviews, and finally assess product quality on the extracted and consumer-concerned features. Experiment results demonstrate the methodology can achieve the assessment of product quality on any feature objectively and quantificationally.

scholarly journals An Affordance-Based Online Review Analysis Framework

2019 ◽  
Vol 1 (1) ◽  
pp. 2457-2466
Author(s):  
Tianjun Hou ◽  
Bernard Yannou ◽  
Yann Leroy ◽  
Emilie Poirson
Keyword(s):  
Opinion Mining ◽  
Online Reviews ◽  
User Preferences ◽  
Data Driven ◽  
Online Review ◽  
Product Reviews ◽  
Analysis Framework ◽  
Review Analysis ◽  
Classical Models ◽  
Feedback Data

AbstractOne of the main tasks of today's data-driven design is to learn customers' concerns from the feedback data posted on the internet, to drive smarter and more profitable decisions during product development. Feature-based opinion mining was first performed by the computer and design scientists to analyse online product reviews. In order to provide more sophisticated customer feedback analyses and to understand in a deeper way customer concerns about products, the authors propose an affordance-based online review analysis framework. This framework allows understanding how and in what condition customers use their products, how user preferences change over years and how customers use the product innovatively. An empirical case study using the proposed approach is conducted with the online reviews of Kindle e-readers downloaded from amazon.com. A set of innovation leads and redesign paths are provided for the design of next-generation e-reader. This study suggests that bridging data analytics with classical models and methods in design engineering can bring success for data-driven design.

scholarly journals Exploring Determinants of Attraction and Helpfulness of Online Product Review: A Consumer Behaviour Perspective

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Xu Chen ◽  
Jie Sheng ◽  
Xiaojun Wang ◽  
Jiangshan Deng
Keyword(s):  
Mixed Method ◽  
Consumer Behaviour ◽  
Online Reviews ◽  
Product Reviews ◽  
Implicit Information ◽  
Online Product Review ◽  
Review Helpfulness ◽  
Research Findings ◽  
The Impact ◽  
Method Approach

To assist filtering and sorting massive review messages, this paper attempts to examine the determinants of review attraction and helpfulness. Our analysis divides consumers’ reading process into “notice stage” and “comprehend stage” and considers the impact of “explicit information” and “implicit information” of review attraction and review helpfulness. 633 online product reviews were collected from Amazon China. A mixed-method approach is employed to test the conceptual model proposed for examining the influencing factors of review attraction and helpfulness. The empirical results show that reviews with negative extremity, more words, and higher reviewer rank easily gain more attraction and reviews with negative extremity, higher reviewer rank, mixed subjective property, and mixed sentiment seem to be more helpful. The research findings provide some important insights, which will help online businesses to encourage consumers to write good quality reviews and take more active actions to maximise the value of online reviews.

scholarly journals Aspect-Based Sentiment Analysis Using Aspect Map

2019 ◽  
Vol 9 (16) ◽  
pp. 3239
Author(s):  
Yunseok Noh ◽  
Seyoung Park ◽  
Seong-Bae Park
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
State Of The Art ◽  
Positional Information ◽  
Specific Aspect ◽  
Classification Model ◽  
Neural Models ◽  
Single Text ◽  
Proposed Model ◽  
Weakly Supervised

Aspect-based sentiment analysis (ABSA) is the task of classifying the sentiment of a specific aspect in a text. Because a single text usually has multiple aspects which are expressed independently, ABSA is a crucial task for in-depth opinion mining. A key point of solving ABSA is to align sentiment expressions with their proper target aspect in a text. Thus, many recent neural models have applied attention mechanisms to learning the alignment. However, it is problematic to depend solely on attention mechanisms to achieve this, because most sentiment expressions such as “nice” and “bad” are too general to be aligned with a proper aspect even through an attention mechanism. To solve this problem, this paper proposes a novel convolutional neural network (CNN)-based aspect-level sentiment classification model, which consists of two CNNs. Because sentiment expressions relevant to an aspect usually appear near the aspect expressions of the aspect, the proposed model first finds the aspect expressions for a given aspect and then focuses on the sentiment expressions around the aspect expressions to determine the final sentiment of an aspect. Thus, the first CNN extracts the positional information of aspect expressions for a target aspect and expresses the information as an aspect map. Even if there exist no data with annotations on direct relation between aspects and their expressions, the aspect map can be obtained effectively by learning it in a weakly supervised manner. Then, the second CNN classifies the sentiment of the target aspect in a text using the aspect map. The proposed model is evaluated on SemEval 2016 Task 5 dataset and is compared with several baseline models. According to the experimental results, the proposed model does not only outperform the baseline models but also shows state-of-the-art performance for the dataset.

scholarly journals Multi-task learning based on question–answering style reviews for aspect category classification and aspect term extraction on GPU clusters

2020 ◽  
Vol 23 (3) ◽  
pp. 1973-1986
Author(s):  
Hanqian Wu ◽  
Siliang Cheng ◽  
Zhike Wang ◽  
Shangbin Zhang ◽  
Feng Yuan
Keyword(s):  
Data Mining ◽  
Cluster Computing ◽  
Question Answering ◽  
Online Reviews ◽  
Product Reviews ◽  
Term Extraction ◽  
Neural Learning ◽  
Proposed Model ◽  
Gpu Clusters ◽  
Training Resources

Abstract Cluster computing technologies are rapidly advancing and user-generated online reviews are booming in the current Internet and e-commerce environment. The latest question–answering (Q&A)-style reviews are novel, abundant and easily digestible product reviews that also contain massive valuable information for customers. In this paper, we mine valuable aspect information of products contained in these reviews on GPU clusters. To achieve this goal, we utilize two subtasks of aspect-based sentiment analysis: aspect term extraction (ATE) and aspect category classification (ACC). Most previous works focused on only one task or solved these two tasks separately, even though they are highly interrelated, and they do not make full use of abundant training resources. To address this problem, we propose a novel multi-task neural learning model to jointly handle these two tasks and explore the performance of our model on GPU clusters. We conducted extensive comparative experiments on an annotated corpus and found that our proposed model outperforms several baseline models in ATE and ACC tasks on GPU clusters, yielding significant strides in data mining for these types of reviews.

An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification

2021 ◽  
pp. 49-56
Author(s):  
Mohammed K. Hassan ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Online Reviews ◽  
Classification Model ◽  
Superior Performance ◽  
Product Reviews ◽  
Metaheuristic Algorithm ◽  
Dragonfly Algorithm ◽  
Hybrid Metaheuristic ◽  
Experimental Values ◽  
Performance Validation

Sentimental Analysis (SA) becomes a familiar topic among business people, which is commonly applied for the classification of sentiments from online reviews. It is generally treated as a sentiment classification (SC) problem where the online reviews are categorized into positive or negative polarities using the words that exist in the online reviews. With this motivation, this paper presents a new K-means clustering with hybrid metaheuristic algorithm (KMC-HMA) for SA and classification. The proposed KMC-HMA technique initially performs data preprocessing to remove the unwanted words from the product reviews. In addition, K-means clustering technique is used for the clustering of the massive quantity of the applied product reviews. Moreover, the clustered data are fed into the classification model based on hybrid ant colony optimization (ACO) with dragonfly algorithm (DFA). The ACO algorithm is used for the classification of product reviews and the performance of the ACO algorithm can be optimally tuned by the use of DFA. The performance validation of the KMC-HMA technique is validated using two datasets such as Canon and ipod. The experimental values pointed out the superior performance of the KMC-HMA technique over the recent state of art techniques.

The Effect of Online Q&As and Product Reviews on Product Performance Metrics: Amazon.com as a Case Study

2021 ◽  
Vol 20 (01) ◽  
pp. 2150005
Author(s):  
Reza Mousavi ◽  
Bidyut Hazarika ◽  
Kuanchin Chen ◽  
Muhammad Razi
Keyword(s):  
Performance Metrics ◽  
Online Reviews ◽  
Review Literature ◽  
Product Performance ◽  
Product Reviews ◽  
Product Sale ◽  
Comparison Shopping ◽  
Questions And Answers ◽  
Review Helpfulness

Online reviews have received an overwhelming interest in the recent decades. Comparatively speaking, the online product questions and answers (Q&As) have received less attention than online reviews, despite that they both affect the image and the value of a project. Although online reviews and Q&As are both forms of user generated knowledge ion online communities, they may affect customers decision making differently. Furthermore, Q&As are very useful for pre-purchase information-searching and comparison shopping, especially when online product reviews either do not provide the needed answer or getting the desired information requires additional “cost” (i.e. time and effort) to sort out. Our findings show that Q&A traits had a varying effect on the product performance. We also found that review helpfulness is another important factor that affects product sale and popularity on e-commerce sites. The present study adds to existing electronic word-of-mouth (eWOM) and product review literature.

A Method of Extracting Opinion from Chinese Product Review

2011 ◽  
Vol 219-220 ◽  
pp. 1513-1517
Author(s):  
Rui Liu ◽  
Yi An ◽  
Lang Song
Keyword(s):  
Opinion Mining ◽  
Online Reviews ◽  
Product Reviews ◽  
Product Review ◽  
Sentiment Lexicon ◽  
Target Attribute

Automatic opinion mining and summarization from online reviews are very useful for customers and merchants. This paper proposes a method to extract opinions from Chinese product reviews. Firstly, reviews are pre-processed and the sentiment features are extracted based on a sentiment lexicon. Then, it finds out the matching target attribute using the extracted sentiment features base on the using co-occurrence knowledge of topic feature and sentiment feature. After the opinions were found, it generates the summary for products according to the most common opinions.

Opinion Mining and Product Review Summarization in E-Commerce

2020 ◽  
pp. 216-243
Author(s):  
Enakshi Jana ◽  
V. Uma
Keyword(s):  
User Experience ◽  
Opinion Mining ◽  
Online Shopping ◽  
Online Reviews ◽  
The Internet ◽  
Product Reviews ◽  
Product Review ◽  
Document Summarization ◽  
Review Summarization ◽  
Product Manufacture

With the immense increase of the number of users of the internet and simultaneously the massive expansion of the e-commerce platform, millions of products are sold online. To improve user experience and satisfaction, online shopping platform enables every user to give their reviews for each and every product that they buy online. Reviews are long and contain only a few sentences which are related to a particular feature of that product. It becomes very difficult for the user to understand other customer views about different features of the product. So, we need accurate opinion-based review summarization which will help both customers and product manufacture to understand and focus on a particular aspect of the product. In this chapter, the authors discuss the abstractive document summarization method to summarize e-commerce product reviews. This chapter has an in-depth explanation about different types of document summarization and how that can be applied to e-commerce product reviews.

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