• Title/Summary/Keyword: Reviews analysis

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SYSTEMIC REVIEWS AND META-ANANLYSIS IN RESEARCH OF THE EVIDENCE-BASED PEDIATRIC DENTISTRY (근거중심 소아치과학 연구의 체계적 고찰과 메타분석)

  • Lee, Kwang-Hee
    • Journal of the korean academy of Pediatric Dentistry
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    • v.33 no.4
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    • pp.728-737
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    • 2006
  • The purpose of study was to introduce the method of systemic reviews and the meta-analysis as the statistical method of the systemic reviews. The resource of study was mainly the Cocharane Handbook for Systematic Reviews of Interventions. The first and most important decision in preparing a review is to determine its focus by asking clearly framed questions. Then searching for databases, quality assessment of the studies, collecting data, analysing and presenting results, and interpreting results fellow. The necessity and principle of meta-analysis were described and the methods of meta-analysis by the data types were summarised.

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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.

A Methodology for Predicting Changes in Product Evaluation Based on Customer Experience Using Deep Learning (딥러닝을 활용한 고객 경험 기반 상품 평가 변화 예측 방법론)

  • An, Jiyea;Kim, Namgyu
    • Journal of Information Technology Services
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    • v.21 no.4
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    • pp.75-90
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    • 2022
  • From the past to the present, reviews have had much influence on consumers' purchasing decisions. Companies are making various efforts, such as introducing a review incentive system to increase the number of reviews. Recently, as various types of reviews can be left, reviews have begun to be recognized as interesting new content. This way, reviews have become essential in creating loyal customers. Therefore, research and utilization of reviews are being actively conducted. Some studies analyze reviews to discover customers' needs, studies that upgrade recommendation systems using reviews, and studies that analyze consumers' emotions and attitudes through reviews. However, research that predicts the future using reviews is insufficient. This study used a dataset consisting of two reviews written in pairs with differences in usage periods. In this study, the direction of consumer product evaluation is predicted using KoBERT, which shows excellent performance in Text Deep Learning. We used 7,233 reviews collected to demonstrate the excellence of the proposed model. As a result, the proposed model using the review text and the star rating showed excellent performance compared to the baseline that follows the majority voting.

The effect of consumer trust on positive online reviews of cosmetics (기능성 화장품의 온라인 사용 후기 신뢰도가 브랜드 선호도, 광고 신뢰도 및 구매의도에 미치는 영향 - 자외선 차단제의 긍정적 사용 후기를 중심으로 -)

  • Park, Ji Hye;Kim, Mi Sook;Hwang, Choon Sup
    • The Research Journal of the Costume Culture
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    • v.25 no.6
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    • pp.831-846
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    • 2017
  • Considering that the effectiveness of ads varies according to the credibility of consumers, it is necessary to establish data regarding consumer credibility in relation to online reviews. To conduct a successful study on the marketing strategies of online reviews, it is also necessary to analyze the relationship between credibility and the various factors that influence the purchase intentions of consumers. Therefore, this study attempted to examine the relationship between consumer trust of on-line reviews, brand preference, ads credibility, and purchase intentions in relation to cosmetics. The study was conducted through a normative descriptive survey method using stimuli and a self-administered questionnaire. Analysis of the structural equation model was conducted for the data analysis. The results revealed that consumer reliance on online reviews of cosmetics influences brand preference, credibility of brand ads and purchase intentions. The results also revealed that consumers' on-line reviews, brand preference, and trust of brand ads are important factors for increasing the purchase intentions. The mediation effect of brand preference and brands' ads credibility were found in the process where on-line reviews exercise an influence on the purchase intentions. It was also found that brand preference has a stronger influence on purchase intention than credibility of brand ads. It was discovered that the credibility of on-line reviews directly influences purchase intentions more than indirectly influences. Considering the results of this study, programs that encourage customers to post on-line reviews, and strategies to promote brand preference by targeting groups that exhibit high trust in online reviews would be recommended.

Sentiment Analysis Model with Semantic Topic Classification of Reviews (리뷰의 의미적 토픽 분류를 적용한 감성 분석 모델)

  • Lim, Myung Jin;Kim, Pankoo;Shin, Ju Hyun
    • Smart Media Journal
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    • v.9 no.2
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    • pp.69-77
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    • 2020
  • Unlike the past, which was limited to terrestrial broadcasts, many dramas are currently being broadcast on cable channels and the Internet web. After watching the drama, viewers actively express their opinions through reviews and studies related to the analysis of these reviews are actively being conducted. Due to the nature of the drama, the genre is not clear, and due to the various age groups of viewers, reviews and ratings from other viewers help to decide which drama to watch. However, since it is difficult for viewers to check and analyze many reviews individually, a data analysis technique is required to automatically analyze them. Accordingly, this paper classifies the topics of reviews that have an important influence on drama selection and reclassifies them into semantic topics according to the similarity of words. In addition, we propose a model that classifies reviews into sentences according to semantic topics and sentiment analysis through sentiment words.

Motives for Writing After-Purchase Consumer Reviews in Online Stores and Classification of Online Store Shoppers (인터넷 점포에서의 구매후기 작성 동기 및 점포 고객 유형화)

  • Hong, Hee-Sook;Ryu, Sung-Min
    • Journal of Distribution Research
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    • v.17 no.3
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    • pp.25-57
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    • 2012
  • This study identified motives for writing apparel product reviews in online stores, and determined what motives increase the behavior of writing reviews. It also classified store customers based on the type of writing motives, and clarified the characteristics of internet purchase behavior and of a demographic profile. Data were collected from 252 females aged 20s' and 30s' who have experience of reading and writing reviews on online shopping. The five types of writing motives were altruistic information sharing, remedying of a grievance and vengeance, economic incentives, helping new product development, and the expression of satisfaction feelings. Among five motives, altruistic information sharing, economic incentives, and helping new product development stimulate writing reviews. Store customers who write reviews were classified into three groups based on their writing motive types: Other consumer advocates(29.8%), self-interested shoppers(40.5%) and shoppers with moderate motives(29.8%). There were significant differences among three groups in writing behavior (the frequency of writing reviews, writing intent of reviews, duration of writing reviews, and frequency of online shopping) and age. Based on results, managerial implications were suggested. Long Abstract : The purpose of present study is to identify the types of writing motives on online shopping, and to clarify the motives affecting the behavior of writing reviews. This study also classifies online shoppers based on the motive types, and identifies the characteristics of the classified groups in terms of writing behavior, frequency of online shopping, and demographics. Use and Gratification Theory was adopted in this study. Qualitative research (focus group interview) and quantitative research were used. Korean women(20 to 39 years old) who reported experience with purchasing clothing online, and reading and writing reviews were selected as samples(n=252). Most of the respondents were relatively young (20-34yrs., 86.1%,), single (61.1%), employed(61.1%) and residents living in big cities(50.9%). About 69.8% of respondents read and 40.5% write apparel reviews frequently or very frequently. 24.6% of the respondents indicated an "average" in their writing frequency. Based on the qualitative result of focus group interviews and previous studies on motives for online community activities, measurement items of motives for writing after-purchase reviews were developed. All items were used a five-point Likert scale with endpoints 1 (strongly disagree) and 5 (strongly agree). The degree of writing behavior was measured by items concerning experience of writing reviews, frequency of writing reviews, amount of writing reviews, and intention of writing reviews. A five-point scale(strongly disagree-strongly agree) was employed. SPSS 18.0 was used for exploratory factor analysis, K-means cluster analysis, one-way ANOVA(Scheffe test) and ${\chi}^2$-test. Confirmatory factor analysis and path model analysis were conducted by AMOS 18.0. By conducting principal components factor analysis (varimax rotation, extracting factors with eigenvalues above 1.0) on the measurement items, five factors were identified: Altruistic information sharing, remedying of a grievance and vengeance, economic incentives, helping new product development, and expression of satisfaction feelings(see Table 1). The measurement model including these final items was analyzed by confirmatory factor analysis. The measurement model had good fit indices(GFI=.918, AGFI=.884, RMR=.070, RMSEA=.054, TLI=.941) except for the probability value associated with the ${\chi}^2$ test(${\chi}^2$=189.078, df=109, p=.00). Convergent validities of all variables were confirmed using composite reliability. All SMC values were found to be lower than AVEs confirming discriminant validity. The path model's goodness-of-fit was greater than the recommended limits based on several indices(GFI=.905, AGFI=.872, RMR=.070, RMSEA=.052, TLI=.935; ${\chi}^2$=260.433, df=155, p=.00). Table 2 shows that motives of altruistic information sharing, economic incentives and helping new product development significantly increased the degree of writing product reviews of online shopping. In particular, the effect of altruistic information sharing and pursuit of economic incentives on the behavior of writing reviews were larger than the effect of helping new product development. As shown in table 3, online store shoppers were classified into three groups: Other consumer advocates (29.8%), self-interested shoppers (40.5%), and moderate shoppers (29.8%). There were significant differences among the three groups in the degree of writing reviews (experience of writing reviews, frequency of writing reviews, amount of writing reviews, intention of writing reviews, and duration of writing reviews, frequency of online shopping) and age. For five aspects of writing behavior, the group of other consumer advocates who is mainly comprised of 20s had higher scores than the other two groups. There were not any significant differences between self-interested group and moderate group regarding writing behavior and demographics.

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Multi-Topic Sentiment Analysis using LDA for Online Review (LDA를 이용한 온라인 리뷰의 다중 토픽별 감성분석 - TripAdvisor 사례를 중심으로 -)

  • Hong, Tae-Ho;Niu, Hanying;Ren, Gang;Park, Ji-Young
    • The Journal of Information Systems
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    • v.27 no.1
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    • pp.89-110
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    • 2018
  • Purpose There is much information in customer reviews, but finding key information in many texts is not easy. Business decision makers need a model to solve this problem. In this study we propose a multi-topic sentiment analysis approach using Latent Dirichlet Allocation (LDA) for user-generated contents (UGC). Design/methodology/approach In this paper, we collected a total of 104,039 hotel reviews in seven of the world's top tourist destinations from TripAdvisor (www.tripadvisor.com) and extracted 30 topics related to the hotel from all customer reviews using the LDA model. Six major dimensions (value, cleanliness, rooms, service, location, and sleep quality) were selected from the 30 extracted topics. To analyze data, we employed R language. Findings This study contributes to propose a lexicon-based sentiment analysis approach for the keywords-embedded sentences related to the six dimensions within a review. The performance of the proposed model was evaluated by comparing the sentiment analysis results of each topic with the real attribute ratings provided by the platform. The results show its outperformance, with a high ratio of accuracy and recall. Through our proposed model, it is expected to analyze the customers' sentiments over different topics for those reviews with an absence of the detailed attribute ratings.

Social Big Data Analysis for Franchise Stores

  • Kim, Hyeon Gyu
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.8
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    • pp.39-46
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    • 2021
  • When conducting social big data analysis for franchise stores, reviews of multiple branches of a franchise can be collected together, from which analysis results can be distorted significantly. To improve its accuracy, it should be possible to filter reviews of other branches properly which are not subject to the analysis. This paper presents a method for social big data analysis which reflects characteristics of franchise stores. The proposed method consists of search key configuration and review filtering. For the former, the open data provided by Small Business Promotion Agency is used to extract region names for collecting reviews more accurately. For the latter, open search APIs provided by Naver or Kakao are used to obtain franchise branch information for filtering reviews of other branches that are not subject to analysis. To verify performance of the proposed method, experiments were conducted based on real social reviews collected from online, where the results showed that the accuracy of the proposed review filtering was 93.6% on the average.

Development of Customer Review Ranking Model Considering Product and Service Aspects Using Random Forest Regression Method

  • Arif Djunaidy;Nisrina Fadhilah Fano
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.8
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    • pp.2137-2156
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    • 2024
  • Customer reviews are the second-most reliable source of information, followed by family and friend referrals. However, there are many existing customer reviews. Some online shopping platforms address this issue by ranking customer reviews according to their usefulness. However, we propose an alternative method to rank customer reviews, given that this system is easily manipulable. This study aims to create a ranking model for reviews based on their usefulness by combining product and seller service aspects from customer reviews. This methodology consists of six primary steps: data collection and preprocessing, aspect extraction and sentiment analysis, followed by constructing a regression model using random forest regression, and the review ranking process. The results demonstrate that the ranking model with service considerations outperformed the model without service considerations. This demonstrates the model's superiority in the three tests, which include a comparison of the regression results, the aggregate helpfulness ratio, and the matching score.

The Impact of Coupang Reviews on Product Sales : Based on FCB Grid Model (쿠팡 리뷰가 상품 매출에 미치는 영향 분석 : FCB Grid Model을 기준으로)

  • Ryu, Sung Gwan;Lee, Ji Young;Lee, Sang Woo
    • The Journal of Information Systems
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    • v.31 no.2
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    • pp.159-177
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    • 2022
  • Purpose Online reviews are critical for sales of online shopping platforms because they provide useful information to consumers. As the eCommerce market grows rapidly, the role of online reviews is becoming more important. The purpose of this study is to analyze how online reviews written by domestic consumers affect product sales by classifying the types of products. Design/methodology/approach This study analyzed how the effects of review characteristics(reviewer reputation, reviewer exposure, review length, time, rating, image, and emotional score) on the usefulness of online reviews differ depending on the product types. Subsequently, how the impact of review attributes (review usefulness, number of reviews, ratings, and emotional scores) on product sales differs according to each product type was compared. Based on the FCB Grid model, the product type was classified into high involvement-rational, high involvement-emotional, low involvement -rational, and low involvement-emotional product types. Findings According to the analysis result, the characteristics of reviews useful to consumers were different for each product type, and the review attributes affecting product sales were also different for each product type. This study confirmed that it revealed that product characteristics are major consideration in evaluating the review usefulness and the factors affecting product sales.