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.
Yihua Zhang;Qinglong Li;Ilyoung Choi;Jaekyeong Kim
Information Systems Review
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제23권1호
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pp.155-172
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2021
With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.
Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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한국정보통신학회 2018년도 춘계학술대회
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pp.460-461
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2018
The traditional recommendation system is developed on the assumption that users behave independently, and have problem of readability and efficiency are inferior due to simply sort products or lack of function for associate product attributes with user's taste. To solve this problem in this study we propose a system that provides user customized information that the analysis of the unstructured review data with the purchase histories of users processed with meaningful information after crawling product review data using text mining with R. This allows to help user make decisions can be provided only necessary information without analyze massive amounts of products review data.
In this paper, we introduce a system that summarizes product evaluation through linguistic analysis to effectively utilize explosively increasing product reviews. Our system analyzes polarities of product reviews by product features, based on which customers evaluate each product like 'design' and 'material' for a skirt product category. The system shows to customers a graph as a review summary that represents percentages of positive and negative reviews. We build an opinion word dictionary for each product feature through context based automatic expansion with small seed words, and judge polarity of reviews by product features with the extracted dictionary. In experiment using product reviews from online shopping malls, our system shows average accuracy of 69.8% in extracting judgemental word dictionary and 81.8% in polarity resolution for each sentence.
Following is discovered in this study by some. First, attribute(product/non- product) of that truth that get through experiment of this study is WOM deduced result that have statistical meaning in dependent variable which is public trust only in this study. Specially, information by word of mouth type received High positive response more with non-product information by word of mouth attribute than the product attribute in all experiment products that is parted according to involvement. Consumer's response by involvement level of product can affect for site attitude, public trust, and intention to purchase by second. For example, consumer's site attitude has induced the most positive response, and can confirm positive response of High consumer most in that involvement sensitivity product in that participation reason type product and could confirm response that is the most positive in that involvement reason product even if there is for intention to purchase. Result of this study is grasped that have High mean value relatively in site attitude, product attitude, information by word of mouth effect, public trust, brand attitude, purchase intention etc. generally that product involvement and information by word of mouth attribute are interrelation by third. This can change fairly data processing process according to consumer's Involvement Level, behavior deterministic process, consumer's action with attitude formation process, and involvement for products. High occasion than is low incline more efforts in information retrieval and is discreet result such as study finding of produce.
The purpose of this study is to analyze examples of online fashion product planning of domestic fashion brands, to grasp the characteristics and step-by-step problems in product planning, and to suggest product planning methods. This study consists of a literature study and a case study. The results of th study are as follows. First, in the information analysis and product planning, product planning according to analysis and targeting of online consumers should be conducted separately from offline, and the proportion of online-only products should be expanded. Second, in the design planning and product development stages, it should be possible to secure the quantity through the pre-planning of fabrics, a to acquire the novelty of the material through the preemption of good fabrics and the pre-planning of colors to secure competitive design. Third, in the convention, a systematic review process involving company members and customer review teams should be conducted to ensure product quality and sales-ability Fourth, in the production stage, the production period must be to reduce cost. Fifth, differentiated services according to the characteristics of their products for each brand in the promotion and sales stages. Based on this analysis, a desirable approach online product planning should first run promotion phase, increasing pre-planning for the product, and organizing specialize work and manpower issues.
One of characteristics of firms having successfully developed new products is that they are practicing strategic learning, that is, organizational learning for preparing the future. In this context, we attempts to examine the effect of strategic learning on the proficiency of new product development activities and new product outcome, in an empirical way. In addition, we Investigated the moderating effect of new product innovativeness on the relationship among strategic learning, proficiency of new product development activities, and new product outcome. The analysis results show that the strategic learning has a positive impact on both the proficiency of new product development activities and new product outcome. And it was found that the impact of strategic learning on the proficiency of new product development activities is increasing when firms developing new products with high degree of innovativeness. However, the impact of strategic learning on new product outcome was not different according to new product innovativeness. This results shed some insight on the role of strategic learning in the new product development management.
This study is to analyze the effect of product involvement on consumer purchase process. From review on previous involvement literatures, enduring product involvement can be defined as "a person's arousal and motivational state activated by product on the basis of his basic value, objective and ego-relevance." To evaluate the effect of involvement on product purchase process, 11 hyphotheses concerning behavioral aspects which is expected to comprise consumer purchase process and to be influenced by degree of involvement were constructed: prepurchase information search, alternative brand comparision, utilization of product attribute, brand differentiation, brand commitment, product knowledge, influence of reference group, dependence on price, price awareness, cognitive dissonance and purchase optimization. Hyphotheses are tested with data from 388 housewives through 20 item involvement scale developed with multi-dimensional perspective on involvement. The scale successfully provides rank order of 8 selected products expected to cover the spectrum of product involvement: formal clothes, coffee, washing machine, shampoo, perfume, detergent, soft drink, pain-reliever as the order of product involvement. Major findings of this study are as follow. First of all, incresed differences were found in hihg involvement product like formal clothes with respect to the degree of prepurchase information search, alternative brand comparision, utilization of product attribute, influence of reference group in any method of analysis. Secondly, invlovement should be interpreted as a consumer characteristic rather than a product one. This means that consumers involve themselves with products, instead products themselves do not have any involvement.
The metacognitive experience of the ease or difficulty with which new, external information can be processed, referred to as 'processing fluency,' has been shown to influence a wide range of human judgments including truth judgments, familiarity judgments, risk perception, evaluation, and preference (see Alter and Oppenheimer 2009 for a review). The current research explores the possibility of a consumer's product innovativeness judgment based on the difficulty of processing new information. In specific, this study examines if the inferential link between (dis)fluency-(un)familiarity can feed into the perception of innovativeness. This study also explores how a consumer's processing motivation can moderate the consumer's reliance on processing fluency in judgments and how the influence of fluency can vary depending on judgment task orders. In an experiment, participants rated a new product's innovativeness and then indicated their product attitude (or vice versa depending on the judgment task order condition) after reading a product review article that was printed in either an easy-to-read or a difficult-to-read font (for fluency manipulation). The findings show that low need for cognition individuals infer higher product innovativeness when processing product information is difficult rather than easy, consistent with the common assumption that 'new information is more difficult to process than familiar information.' The findings also suggest that once low fluency is attributed to innovativeness, it may no longer lead to a negative response to the product. High need for cognition individuals' judgments on product innovativeness are not affected by fluency. The findings also demonstrate a judgment task order effect on the use of fluency in judgments (e.g., Xu and Schwarz 2005). This study provides the first evidence that an individual's fluency experience can be used as a source of information in product innovativeness judgments especially under low processing motivation conditions. The findings can help marketers better understand the malleability of consumer judgments and perceptions of product characteristics (e.g., product innovativeness) by demonstrating an interesting interplay of processing fluency, processing motivation, and judgment task-related contextual factors.
Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.
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