Personal customized recommendation system reflecting purchase criteria and product reviews sentiment analysis

As the size of the e-commerce market grows, the consequences of it are appearing throughout society.The business Environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the users subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user. Recommender system (RS) has emerged as a major research interest that Aims to help users to find items online by providing suggestions that Closely match their interest. This paper provides a comprehensive study on the RS covering the different recommendation approaches, associated issues, and techniques used for information retrieval.

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Product recommendation system based user purchase criteria and product reviews

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i6.pp5454-5462 ◽

2019 ◽

Vol 9 (6) ◽

pp. 5454

Author(s):

Jinyoung Kim ◽

Doyeun Hwang ◽

Hoekyung Jung

Keyword(s):

Morphological Analysis ◽

Recommendation System ◽

Product Evaluation ◽

Shopping Mall ◽

Product Reviews ◽

Purchasing Decisions ◽

Product Review ◽

Product Recommendation ◽

Average Value ◽

Purchase Criteria

In this paper, we propose a system that provides customized product recommendation information after crawling product review data of internet shopping mall with unstructured data, morphological analysis using Python. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. And extracts and analyzes only the review including the purchase criterion selected by the user among the product reviews left by other users. The positive and negative evaluations contained in the extracted product review data are quantified and using the average value, we extract the top 10 products with good product evaluation, sort and recommend to users. And provides user-customized information that reflects the user's preference by arranging and providing a center around the criteria that the user occupies the largest portion of the product purchase. This allows users to reduce the time it takes to purchase a product and make more efficient purchasing decisions.

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Aspect-Based Sentiment Analysis of Online Product Reviews

Advances in Business Information Systems and Analytics - Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence ◽

10.4018/978-1-5225-2031-3.ch010 ◽

2017 ◽

pp. 175-191 ◽

Cited By ~ 1

Author(s):

Vinod Kumar Mishra ◽

Himanshu Tiruwa

Keyword(s):

Sentiment Analysis ◽

Computational Linguistics ◽

Language Processing ◽

Future Trend ◽

Product Reviews ◽

Customer Reviews ◽

Part Of Speech ◽

Lexical Approach ◽

Sentiment Score ◽

Pos Tagger

Sentiment analysis is a part of computational linguistics concerned with extracting sentiment and emotion from text. It is also considered as a task of natural language processing and data mining. Sentiment analysis mainly concentrate on identifying whether a given text is subjective or objective and if it is subjective, then whether it is negative, positive or neutral. This chapter provide an overview of aspect based sentiment analysis with current and future trend of research on aspect based sentiment analysis. This chapter also provide a aspect based sentiment analysis of online customer reviews of Nokia 6600. To perform aspect based classification we are using lexical approach on eclipse platform which classify the review as a positive, negative or neutral on the basis of features of product. The Sentiwordnet is used as a lexical resource to calculate the overall sentiment score of each sentence, pos tagger is used for part of speech tagging, frequency based method is used for extraction of the aspects/features and used negation handling for improving the accuracy of the system.

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Product weakness finder: an opinion-aware system through sentiment analysis

Industrial Management & Data Systems ◽

10.1108/imds-05-2014-0159 ◽

2014 ◽

Vol 114 (8) ◽

pp. 1301-1320 ◽

Cited By ~ 22

Author(s):

Hongwei Wang ◽

Wei Wang

Keyword(s):

Sentiment Analysis ◽

Comparative Evaluation ◽

Analytical Framework ◽

Online Reviews ◽

Product Reviews ◽

Network Algorithms ◽

Content Type ◽

Sentiment Mining ◽

Highly Correlated ◽

Sentiment Score

Purpose – Extant methods of product weakness detection usually depend on time-consuming questionnaire with high artificial involvement, so the efficiency and accuracy are not satisfied. The purpose of this paper is to propose an opinion-aware analytical framework – PRODWeakFinder – to expect to detect product weaknesses through sentiment analysis in an effective way. Design/methodology/approach – PRODWeakFinder detects product weakness by considering both comparative and non-comparative evaluations in online reviews. For comparative evaluation, an aspect-oriented comparison network is built, and the authority is assessed for each node by network analysis. For non-comparative evaluation, sentiment score is calculated through sentiment analysis. The composite score of aspects is calculated by combing the two types of evaluations. Findings – The experiments show that the comparative authority score and the non-comparative sentiment score are not highly correlated. It also shows that PRODWeakFinder outperforms the baseline methods in terms of accuracy. Research limitations/implications – Semantic-based method such as ontology are expected to be applied to identify the implicit features. Furthermore, besides PageRank, other sophisticated network algorithms such as HITS will be further employed to improve the framework. Practical implications – The link-based network is more suitable for weakness detection than the weight-based network. PRODWeakFinder shows the potential on reducing overall costs of detecting product weaknesses for companies. Social implications – A quicker and more effective way would be possible for weakness detection, enabling to reduce product defects and improve product quality, and thus raising the overall social welfare. Originality/value – An opinion-aware analytical framework is proposed to sentiment mining of online product reviews, which offer important implications regarding how to detect product weaknesses.

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A Survey on E-Commerce Platform for Quality Evaluation Using Product Reviews

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-933 ◽

2021 ◽

pp. 74-77

Author(s):

Gautami Tilve ◽

Krutika Valanj ◽

Aishwarya Bhor ◽

Vaibhav Waghmare ◽

Prof. R. S. Shishupal

Keyword(s):

Product Quality ◽

Sentiment Analysis ◽

Quality Evaluation ◽

Product Reviews ◽

Quality Service ◽

Delivery Time ◽

Product Review ◽

The Past ◽

The Impact ◽

Relevant Factors

It has been seen that there is wide acceleration for an E-commerce platform over the past 10 years. Moreover the E-commerce platform booms in the last year due to this COVID -19 pandemic and potentially the next couple of months. Product Review helps a lot for buying anything online regarding product quality, Service, or delivery time. Sentiment analysis helps to understand the context and the person's intent about the product like +ve, -ve, or Neutral. This paper gives the survey of techniques used by the researcher to identify the most relevant factors by taking into account the frequency of the aspect and the impact of customers at the same time. The abstract view of the proposed system that we are going to implement helps to find a positive, negative, or neutral sense of aspects of the product.

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Personalized Recommendation Algorithm Based on Product Reviews

Journal of Electronic Commerce in Organizations ◽

10.4018/jeco.2018070103 ◽

2018 ◽

Vol 16 (3) ◽

pp. 22-38

Author(s):

Zhibo Wang ◽

Mengyuan Wan ◽

Xiaohui Cui ◽

Lin Liu ◽

Zixin Liu ◽

...

Keyword(s):

Recommendation System ◽

Information Overload ◽

Recall Rate ◽

Personalized Recommendation ◽

The Internet ◽

Product Reviews ◽

Recommendation Algorithm ◽

Preference Model ◽

Product Features ◽

Huge Data

Under the background of leap-forward development for the internet, e-commerce has played an important role in people's daily life, but huge data sizes have also brought problems, such as information overload which can be solved by using a recommendation system effectively. However, with the development of the e-commerce, the amount of the product catalogs and users becomes larger, which causes lower performance of the traditional recommendation system. This article comes up with a personalized recommendation algorithm based on the data mining of product reviews to optimize the performance of the new recommendation system. Features of the product were extracted, for which the users' sentiment polarity was analyzed. This article develops a recommendation system based on the user's preference model and the product features to get the recommendation result. Experimental results show that a personalized recommendation has significantly improved the accuracy and recall rate when compared with a traditional recommendation algorithm.

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Aspect-Based Sentiment Analysis of Online Product Reviews

Natural Language Processing ◽

10.4018/978-1-7998-0951-7.ch003 ◽

2020 ◽

pp. 31-47

Author(s):

Vinod Kumar Mishra ◽

Himanshu Tiruwa

Keyword(s):

Sentiment Analysis ◽

Computational Linguistics ◽

Language Processing ◽

Future Trend ◽

Product Reviews ◽

Customer Reviews ◽

Part Of Speech ◽

Lexical Approach ◽

Sentiment Score ◽

Pos Tagger

Sentiment analysis is a part of computational linguistics concerned with extracting sentiment and emotion from text. It is also considered as a task of natural language processing and data mining. Sentiment analysis mainly concentrate on identifying whether a given text is subjective or objective and if it is subjective, then whether it is negative, positive or neutral. This chapter provide an overview of aspect based sentiment analysis with current and future trend of research on aspect based sentiment analysis. This chapter also provide a aspect based sentiment analysis of online customer reviews of Nokia 6600. To perform aspect based classification we are using lexical approach on eclipse platform which classify the review as a positive, negative or neutral on the basis of features of product. The Sentiwordnet is used as a lexical resource to calculate the overall sentiment score of each sentence, pos tagger is used for part of speech tagging, frequency based method is used for extraction of the aspects/features and used negation handling for improving the accuracy of the system.

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Sentiment Analysis on Tokopedia Product Online Reviews Using Random Forest Method

E3S Web of Conferences ◽

10.1051/e3sconf/202020216006 ◽

2020 ◽

Vol 202 ◽

pp. 16006

Author(s):

Stephenie ◽

Budi Warsito ◽

Alan Prahutama

Keyword(s):

Random Forest ◽

Sentiment Analysis ◽

Online Reviews ◽

Consumer Products ◽

Classification Model ◽

Product Reviews ◽

Validation Data ◽

Random Forest Method ◽

Sentiment Score ◽

Negative Sentiment

Tokopedia is one of the most popular e-commerce sites in Indonesia that offers consumer products from various categories. In each product section, a review feature is offered. This review feature became essential in evaluating the sellers and become one consideration for customers in making purchase consideration. Sentiment analysis of Tokopedia product reviews may provide the opportunity to look on how Tokopedia customers respond to product quality and sellers’ hospitality. In evaluating the model, the reviews were grouped as: “positive sentiment” and “negative sentiment” using the Random Forest method and 10-fold cross-validation. Data labelling was carried out automatically by calculating the sentiment score using Lexicon-Based. Visualization of the labelling results was then done using a bar graph and a word cloud on each class of sentiment in order to look up for information that is considered important and most discussed. The test results showed that the accuracy of the Random Forest Method with parameter mtry = 73 and ntree = 50 is 97.38% which leads to the conclusion that the Random Forest Method could well predict the product reviews of Tokopedia. The greater the accuracy, the better performance of the classification model.

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Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm

IJIIS: International Journal of Informatics and Information Systems ◽

10.47738/ijiis.v2i2.13 ◽

2019 ◽

Vol 2 (2) ◽

pp. 48-55

Author(s):

Taqwa Hariguna ◽

Wiga Maulana Baihaqi ◽

Aulia Nurwanti

Keyword(s):

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Product Reviews ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Product Review ◽

Negative Comment ◽

Negative Comments

In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive comments

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Semantic Features for Optimizing Supervised Approach of Sentiment Analysis on Product Reviews

Computers ◽

10.3390/computers8030055 ◽

2019 ◽

Vol 8 (3) ◽

pp. 55

Author(s):

Bagus Setya Rintyarna ◽

Riyanarto Sarno ◽

Chastine Fatichah

Keyword(s):

Sentiment Analysis ◽

Product Information ◽

Word Sense Disambiguation ◽

Online Reviews ◽

Semantic Features ◽

Product Reviews ◽

Word Sense ◽

Product Review ◽

Online Product Review ◽

Text Features

The growth of ecommerce has triggered online reviews as a rich source of product information. Revealing consumer sentiment from the reviews through Sentiment Analysis (SA) is an important task of online product review analysis. Two popular approaches of SA are the supervised approach and the lexicon-based approach. In supervised approach, the employed machine learning (ML) algorithm is not the only one to influence the results of SA. The utilized text features also handle an important role in determining the performance of SA tasks. In this regard, we proposed a method to extract text features that takes into account semantic of words. We argue that this semantic feature is capable of augmenting the results of supervised SA tasks compared to commonly utilized features, i.e., bag-of-words (BoW). To extract the features, we assigned the correct sense of the word in reviewing the sentence by adopting a Word Sense Disambiguation (WSD) technique. Several WordNet similarity algorithms were involved, and correct sentiment values were assigned to words. Accordingly, we generated text features for product review documents. To evaluate the performance of our text features in the supervised approach, we conducted experiments using several ML algorithms and feature selection methods. The results of the experiments using 10-fold cross-validation indicated that our proposed semantic features favorably increased the performance of SA by 10.9%, 9.2%, and 10.6% of precision, recall, and F-Measure, respectively, compared with baseline methods.

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