• Title, Summary, Keyword: Product Review

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Positioning of Smart Speakers by Applying Text Mining to Consumer Reviews: Focusing on Artificial Intelligence Factors (텍스트 마이닝을 활용한 스마트 스피커 제품의 포지셔닝: 인공지능 속성을 중심으로)

  • Lee, Jung Hyeon;Seon, Hyung Joo;Lee, Hong Joo
    • Knowledge Management Research
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    • v.21 no.1
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    • pp.197-210
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    • 2020
  • The smart speaker includes an AI assistant function in the existing portable speaker, which enables a person to give various commands using a voice and provides various offline services associated with control of a connected device. The speed of domestic distribution is also increasing, and the functions and linked services available through smart speakers are expanding to shopping and food orders. Through text mining-based customer review analysis, there have been many proposals for identifying the impact on customer attitudes, sentiment analysis, and product evaluation of product functions and attributes. Emotional investigation has been performed by extracting words corresponding to characteristics or features from product reviews and analyzing the impact on assessment. After obtaining the topic from the review, the effect on the evaluation was analyzed. And the market competition of similar products was visualized. Also, a study was conducted to analyze the reviews of smart speaker users through text mining and to identify the main attributes, emotional sensitivity analysis, and the effects of artificial intelligence attributes on product satisfaction. The purpose of this study is to collect blog posts about the user's experiences of smart speakers released in Korea and to analyze the attitudes of customers according to their attributes. Through this, customers' attitudes can be identified and visualized by each smart speaker product, and the positioning map of the product was derived based on customer recognition of smart speaker products by collecting the information identified by each property.

Product Feature Extraction and Rating Distribution Using User Reviews (사용자 리뷰를 이용한 상품 특징 추출 및 평점 분배)

  • Son, Soobin;Chun, Jonghoon
    • The Journal of Society for e-Business Studies
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    • v.22 no.1
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    • pp.65-87
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    • 2017
  • We propose a method to analyze the user reviews and ratings of the products in the online shopping mall and automatically extracts the features of the products to determine the characteristics of a product. By judging whether a rating is given by a specific feature of a product, our method distributes the score to each feature. Conventional methods force users to wastes time reading overflowing number of reviews and ratings to decide whether to buy the product or not. Moreover, it is difficult to grasp the merits and demerits of the product, because of the way reviews and ratings are provided. It is structured in a way that it is impossible to decide which rating is given to the which characteristics of the product. Therefore, in this paper, to resolve this problem, we propose a method to automatically extract the feature of the product from the user review and distribute the score to appropriate characteristics of the product by calculating the rating of each feature from the overall rating. proposed method collects product reviews and ratings, conducts morphological analysis, and extracts features and emotional words of the products. In addition, a method for determining the polarity of a sentence in which the feature appears is given a weight value for each feature. results of the experiment and the questionnaires comparing the existing methods show the usefulness of the proposed method. We also validates the results by comparing the analysis conducted by the product review experts.

Automatic Product Feature Extraction for Efficient Analysis of Product Reviews Using Term Statistics (효율적인 상품평 분석을 위한 어휘 통계 정보 기반 평가 항목 추출 시스템)

  • Lee, Woo-Chul;Lee, Hyun-Ah;Lee, Kong-Joo
    • The KIPS Transactions:PartB
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    • v.16B no.6
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    • pp.497-502
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    • 2009
  • In this paper, we introduce an automatic product feature extracting system that improves the efficiency of product review analysis. Our system consists of 2 parts: a review collection and correction part and a product feature extraction part. The former part collects reviews from internet shopping malls and revises spoken style or ungrammatical sentences. In the latter part, product features that mean items that can be used as evaluation criteria like 'size' and 'style' for a skirt are automatically extracted by utilizing term statistics in reviews and web documents on the Internet. We choose nouns in reviews as candidates for product features, and calculate degree of association between candidate nouns and products by combining inner association degree and outer association degree. Inner association degree is calculated from noun frequency in reviews and outer association degree is calculated from co-occurrence frequency of a candidate noun and a product name in web documents. In evaluation results, our extraction method showed an average recall of 90%, which is better than the results of previous approaches.

The Effect of Review Behavior on the Reviewer's Valence in Online Retailing

  • Oh, Yun-Kyung
    • The Journal of Distribution Science
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    • v.15 no.10
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    • pp.41-50
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    • 2017
  • Purpose - Online product review has become a crucial part of the online retailer's market performance for a wide range of products. This research aims to investigate how an individual reviewer's review frequency and timing affect her/his average attitude toward products. Research design, data, and methodology - To conduct reviewer-level analysis, this study uses 42,172 posted online review messages generated by 6,941 identified reviewers for 59 movies released in the South Korea from July 2015 to December 2015. This study adopts Tobit model specification to take into account the censored nature and the selection bias arising from the nature of J-shaped distribution of movie rating. Results - Our estimation results support that the negative impact of review frequency and timing on valence. Furthermore, review timing has an inverted-U relationship with the user's average valence and enhance the negative effect of review frequency. Conclusions - This study contributes to the growing literature on the understanding how eWOM is generated at the individual consumer level. On the basis of the main empirical findings, this study provides insights into building a recommendation system in online retail store based on the consumer's review history data - frequency, timing, and valence.

Oriental medicine mangifera indica

  • Rai, Sujay;Basak, Souvik;Mukherjee, Kakali;Saha, BP;Mukherjee, Pulok K
    • Oriental Pharmacy and Experimental Medicine
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    • v.7 no.1
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    • pp.1-10
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    • 2007
  • Mangifera indica Linn. (MI) (Family: Anacardiaceae) is commonly known worldwide as mango and ‘Aam’ in India. MI shares an important place in treatment of several diseases in various ancient system of medicine like Ayurveda and other Indian System of Medicine and some other Traditional Medicines world wide. Almost all the parts of MI have been used in oriental medicine, so in this review attempt has being made to review the history, traditional uses, phytoconstituents and therapeutic potentials of mango.

Retrieving Minority Product Reviews Using Positive/Negative Skewness (긍정/부정 비대칭도를 이용한 소수상품평의 검색)

  • Cho, Heeryon;Lee, Jong-Seok
    • KIPS Transactions on Software and Data Engineering
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    • v.4 no.3
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    • pp.121-128
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    • 2015
  • A given product's online product reviews build up to form largely positive or negative reviews or mixed reviews that include both the positive and negative reviews. While the homogeneously positive or negative reviews help readers identify the generally praised or criticized product, the mixed reviews with minority opinions potentially contain valuable information about the product. We present a method of retrieving minority opinions from the online product reviews using the skewness of positive/negative reviews. The proposed method first classifies the positive/negative product reviews using a sentiment dictionary and then calculates the skewness of the classified results to identify minority reviews. Minority review retrieval experiments were conducted on smartphone and movie reviews, and the F1-measures were 24.6% (smartphone) and 15.9% (movie) and the accuracies were 56.8% and 46.8% when the individual reviews' sentiment classification accuracies were 85.3% and 78.8%. The theoretical performance of minority review retrieval is also discussed.

Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

  • Akram, Abubakker Usman;Khan, Hikmat Ullah;Iqbal, Saqib;Iqbal, Tassawar;Munir, Ehsan Ullah;Shafi, Dr. Muhammad
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.10
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    • pp.5120-5142
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    • 2018
  • Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area. Existing studies carried out for review spam detection have exploited feature engineering approach; however limited number of features are considered. This paper proposes a Feature-Centric Model for Review Spam Detection (FMRSD) to detect spam reviews. The proposed model examines a wide range of feature sets including ratings, sentiments, content, and users. The experimentation reveals that the proposed technique outperforms the baseline and provides better results.

Identifying Factors Affecting Helpfulness of Online Reviews: The Moderating Role of Product Price (제품 가격에 따른 온라인 리뷰 유익성 결정 요인에 관한 연구)

  • Baek, Hyun-Mi;Ahn, Joong-Ho;Ha, Sang-Wook
    • The Journal of Society for e-Business Studies
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    • v.16 no.3
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    • pp.93-112
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    • 2011
  • For the success of an online retail market, it is important to allow consumers to get more helpful reviews by figuring out the factors determining the helpfulness of online reviews. On the basis of elaboration likelihood model, this study analyzes which factors determine the helpfulness of reviews and how the factors affecting the helpfulness of an online consumer review differ for product price. For this study, 75,226 online consumer reviews were collected from Amazon.com. Furthermore, additional information on review messages was also gathered by carrying out a content analysis on the review messages. This study shows that both of peripheral cues such as review rating and reviewer's credibility and central cues such as word count of review message and the proportion of negative words influence the helpfulness of review. In addition, the result of this study reveals that each consumer focuses on different information sources of reviews depending on the product price.

Increasing Accuracy of Classifying Useful Reviews by Removing Neutral Terms (중립도 기반 선택적 단어 제거를 통한 유용 리뷰 분류 정확도 향상 방안)

  • Lee, Minsik;Lee, Hong Joo
    • Journal of Intelligence and Information Systems
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    • v.22 no.3
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    • pp.129-142
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    • 2016
  • Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

Online Reviews Analysis for Prediction of Product Ratings based on Topic Modeling (토픽 모델링에 기반한 온라인 상품 평점 예측을 위한 온라인 사용 후기 분석)

  • Park, Sang Hyun;Moon, Hyun Sil;Kim, Jae Kyeong
    • Journal of Information Technology Services
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    • v.16 no.3
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    • pp.113-125
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    • 2017
  • Customers have been affected by others' opinions when they make a purchase. Thanks to the development of technologies, people are sharing their experiences such as reviews or ratings through online or social network services, However, although ratings are intuitive information for others, many reviews include only texts without ratings. Also, because of huge amount of reviews, customers and companies can't read all of them so they are hard to evaluate to a product without ratings. Therefore, in this study, we propose a methodology to predict ratings based on reviews for a product. In a methodology, we first estimate the topic-review matrix using the Latent Dirichlet Allocation technic which is widely used in topic modeling. Next, we predict ratings based on the topic-review matrix using the artificial neural network model which is based on the backpropagation algorithm. Through experiments with actual reviews, we find that our methodology can predict ratings based on customers' reviews. And our methodology performs better with reviews which include certain opinions. As a result, our study can be used for customers and companies that want to know exactly a product with ratings. Moreover, we hope that our study leads to the implementation of future studies that combine machine learning and topic modeling.