• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.101-109
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• 2023

This paper discusses the use of sentiment analysis and text summarization techniques to extract valuable information from the large volume of user-generated content such as reviews, comments, and feedback on online platforms and social media. The paper highlights the effectiveness of sentiment analysis in identifying positive and negative reviews and the importance of summarizing such text to facilitate comprehension and convey essential findings to readers. The proposed work focuses on summarizing all positive and negative reviews to enhance product quality, and the performance of the generated summaries is measured using ROUGE scores. The results show promising outcomes for the developed methods in summarizing user-generated content.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.83-92
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• 2023

The majority of product users rely on the reviews that are posted on the appropriate website. Both users and the product's manufacturer could benefit from these reviews. Daily, thousands of reviews are submitted; how is it possible to read them all? Sentiment analysis has become a critical field of research as posting reviews become more and more common. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM, CNN, RNN, and GRU. Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. According to experimental results utilizing a publicly accessible dataset with reviews for all of the models, both positive and negative, and CNN, the best model for the dataset was identified in comparison to the other models, with an accuracy rate of 81%.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.177-181
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• 2023

User reviews posted in the application market show high relevance with the satisfaction of application users and its significance has been proven from numerous studies. User reviews are also crucial data as they are essential for improving applications after its release. However, as infinite amounts of user reviews are posted per day, application developers are unable to examine every user review and address them. Simply addressing the reviews in a chronological order will not be enough for an adequate user satisfaction given the limited resources of the developers. As such, the following research suggests a systematical method of analyzing user reviews with a cost-benefit analysis, in which the benefit of each user review is quantified based on the number of positive/negative words and the cost of each user review is quantified by using function point, a technique that measures software size.

• Asia Marketing Journal
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• 제26권1호
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• pp.23-30
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• 2024

Firms often engage in manipulating online reviews as a promotional activity to influence consumers' evaluation on their products. With the prevalence of the promotional activities, consumers may notice and discount the reviews generated by the promotional activities. Discounting the firm-generating reviews may cause systematic measurement errors in the valence variable and lead to a negative bias when estimating the effect of consumers' organic reviews on demand. To correct the bias, this study proposes including product-specific bias-correction terms representing the proportion of extreme reviews in analysis. For illustration, the proposed method is applied to a demand model for data of movies released in South Korea. The results confirm a negative bias in the estimate of the valence sensitivity of demand. The negative bias potentially leads to an underestimation of the magnitude of the contagion effect through social interactions, a key component of evaluating the value of a satisfied consumer.

• 한국의류학회지
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• 제36권3호
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• pp.282-296
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• 2012

This study identified the types of motives for reading consumer reviews of apparel products for online stores and classified shoppers into the groups based on motives. Data were collected from eleven Korean women by a focus group interview and from 313 females by an online survey. Respondents were in their 20s' and 30s' with significant experience reading consumer reviews of apparel products for online stores. The seven motives found by interviews were reduced to four types of motives by factor analysis: Right product choice and judgment of product value, risk reduction, saving time and money, and fun/killing time. The motive for the right product choice and judgment of product value was the highest and the motive for fun/killing time was the lowest. Consumers were classified into four groups based on motives: Utilitarian shoppers (25.8%), shopping-task oriented shoppers (36.8%), multiple-motive shoppers (19.7%), and moderate-motive shoppers (17.7%). There were significant differences among age groups and the amount of reading reviews posted on a product and the duration of reading reviews for online stores. In addition, managerial implications were developed.

• Asia pacific journal of information systems
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• 제25권4호
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• pp.784-804
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• 2015

This study investigates the effects of a two-sided message on product attitude and purchase intention by using a message structure variable, such as attribute importance in the context of online consumer reviews (OCRs). Study 1 explains the previous inconsistent results of a two-side message by comparing a one-side message and a two-side message by using the attribute importance in negative reviews. Study 2 determines the reasons for the inconsistent results of a refutational two-sided message research by using the attribute importance in negative reviews and website trust. Two experiments are designed to test our hypotheses. The first experiment is a $2{\times}2$ factorial design with 84 participants. The second experiment uses a $2{\times}2{\times}2$ factorial design with 196 participants. In study 1, two-sided OCRs are more credible than one-sided OCRs, and two-sided OCRs that use low important attributes are more effective in making favorable product attitude/purchase intention. In study 2, refutational two-sided OCRs that use high attribute importance render positive effects on product attitudes in trustworthy websites. However, the refutation could negatively affect product attitude/purchase intention in low trustworthy websites.

• Asia pacific journal of information systems
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• 제25권4호
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• pp.763-783
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• 2015

Online customer reviews (i.e., electronic word-of-mouth) has gained considerable interest over the past years. However, a knowledge gap exists in explaining the mechanisms among the factors that determine the product sales in online retailing environment. To fill the gap, this study adopts a principal-agent perspective to investigate the effect of customer reviews and customer incentives on product sales in online retail stores. Two customer review factors (i.e., average review ratings and the number of reviews) and two customer incentive factors (i.e., price discounts and special shipping offers) are used to predict product sales in regression analysis. The sales ranking data collected from the video game titles at Amazon.com are used to analyze the direct effects of the four factors and the interaction effects between customer review and customer incentive factors to product sales. Result reveals that most relationships exist as hypothesized. The findings support both the direct and interaction effects of customer reviews and incentive factors on product sales. Based on the findings, discussions are provided with regard to the academic and practical contributions.

• Asia pacific journal of information systems
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• 제25권4호
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• pp.746-762
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• 2015

This study developed a conceptual model that integrated psychological and physical reactions resulting from online tourism reviews through a longitudinal trust-satisfaction model (LSTM) developed based on the extended valence framework and expectation-confirmation theory. Online reviews are essential factor of consumer's purchase decision. This phenomenon is well applied in a tourism context. However, investigations on online reviews in a longitudinal approach in a tourism context are quite limited. Therefore, this study suggests a conceptual model based on LTSM and several propositions about how online tourism reviews, which are divided into factual and experiential reviews, influence the future travelers' perceptions and attitudes, such as expectation, confirmation, and destination loyalty, in a longitudinal format by examining previous related studies. Finally, expected results were discussed and several implications were described theoretically and practically.

• 한국IT서비스학회지
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• 제11권3호
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• pp.103-127
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• 2012

With the advent of SNS (Social Network Services), the product reviews by friends in SNS are intensively utilized for online marketing. However, there is a lack of empirical evidence on the actual marketing effect of SNS reviews, although we need to identify who can be the target of SNS marketing in terms of customer attributes, preferences, or experiences. In this study, we investigate the moderating role of customer attributes in identifying the effect of SNS reviews on customer purchasing decision. As the moderating variables, we adopt 'information search experience' and 'perception of information overload'. Research results evidence that, in order to understand the effect of SNS reviews in a comprehensive manner, we need to examine it in the context of various related factors such as 'information search experience' and 'perception of information overload'. The results show that the persuading effect of SNS reviews for product purchasing is stronger for the customers with the lower information search experiences as well as the lower perception on the information overload on the web. This result delivers managerial implications on who can be the target customers of SNS marketing.

• International Journal of Contents
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• 제14권3호
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• pp.46-52
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• 2018

The objective of this study was to determine the status of star chart game applications in the Google play store in Korea. The share of game genres in paid and free star charts of game applications was searched. Also, the average reviewer's rating, average number of reviews, and average age rating based on the genre of paid and free star charts of game applications, and the average price of paid applications based on genre were analyzed. Hypothesis tests for the differences in average reviewer's rating, average number of reviews, average age rating according to the genre of game applications were performed. Also, hypothesis tests for the differences in average reviewer's rating, average number of reviews, average age rating between the paid and free game applications along with the hypothesis test for the differences in price according to the genre of paid game applications were performed. Lastly, hypothesis tests for the correlation between the start chart ranking and number of reviews in association with the correlation between the start chart ranking and reviewer's rating were performed. Statistically significant differences in average reviewer's rating, average number of reviews, average age rating according to the genre of game applications, and between the paid and free game applications were verified. However, the correlation between the start chart ranking and number of reviews in association with the correlation between the start chart ranking and reviewer's rating were not statistically significant.