Formulating Strategies from Consumer Opinion Analysis on AI Kids Phone using Text Mining
In order to come up with satisfying product and improvement, firms use traditional marketing research methods to obtain consumers’ opinions and further try to reflect them. Recently, gathering data from consumer communication platforms like internet and SNS has become popular methods. Meanwhile, with the development of information technology, mobile companies are launching new digital products for children to protect them from harmful content and provide them with necessary functions and information. Among these digital products, Kids Phone, which is a wearable device with safe functions that enable parents to learn childern’s location. Kids phone is relatively cheaper and simpler than smartphone but it is noted that there are several problems such as some useless functions and frequent breakdowns. This study analyzes the reviews of Kids phones from domestic mobile companies, identifies the characteristics, strengths and weaknesses of the products, proposes improvement methods strategies for devices and services through SNS consumer analysis. In order to do that customer review data from online shopping malls was gathered and was further analyzed through text mining methods such as TF/IDF, Sentiment Analysis, and network analysis. Customer review data was gathered through crawling Online shopping Mall and Naver Blog/Café. Data analysis and visualization was done using ‘R’, ‘Textom’, and ‘Python’. Such analysis allowed us to figure out main issues and recent trends regarding kids phones and to suggest possible service improvement strategies based on sentiment analysis.
Asay, P. A. and Lal, A., “Who’s Googled whom? Trainees’ Internet and online social networking experiences, behaviors, and attitudes with clients and supervisors,” Training and Education in Professional Psychology, Vol. 8, No. 2, pp. 105-111, 2014.
Dellarocas, C. and Narayan, R., “A statistical measure of a population’s propensity to engage in post-purchase online word of mouth,” Statistical Science, Vol. 21, No. 2, pp. 277-285, 2006.
Derbaix, C. and Vanhamme, J., “Inducing word of mouth by eliciting surprise: A pilot investigation,” Journal of Economic Psychology, Vol. 24, No. 1, pp. 99-116, 2003.
Holsapple, C. W., Hsiao, S., and Pakath, R., “Business social media analytics: Characterization and conceptual framework,” Decision Support Systems, Vol, 110, pp. 32-45, 2018.
Hong, S. C., “An N-gram Analysis of Asian Learners’ Writing: Focusing on Grammatical and Functional Perspectives,” Journal of Language Sciences, Vol. 124, No. 1, pp. 191-215, 2017.
Hong, S. C., “An n-gram Analysis of Maritime English,” The Journal of Linguistics Science, Vol. 61, pp. 283-302, 2012.
Hu, N., Koh, N. S., and Reddy, S. K., “Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales,” Decision Support System, Vol. 57, No. 1, pp. 42-53, 2014.
Hu, N., Zhang, J., and Pavlou, P. A., “Overcoming the J-Shaped distribution of product reviews,” Communications of the ACM, Vol. 52, No. 10, pp. 144-147, 2009.
Hwangbo, H. W. and Kim, J. H., “A Study on the Factors Affecting to the Export Performance for Korean Drama Using Sentimental Analysis,” The e-Business Studies, Vol. 17, No. 6, pp. 87-99, 2016.
Jackson, T., “Prosperity without growth: Economics for a finite planet,” Information Theory IEEE T., Vol. 56, No. 10, pp. 4956-4980, 2009.
Jo, T. H., “Concepts and Applications of Text Mining,” Journal of Scientific & Technological Knowledge Infrastructure, Vol. 5, pp. 76-85, 2001.
Kang, H. J., Park, S. B., Kim, J. S., and Park, B. H., “FPS game success factor analysis,” Korea Intelligent Information System Society, pp. 98-99, 2017.
Kang, T. Y. and Park D. H., “The Effect of Expert Reviews on Consumer Product Evaluations: A Text Mining Approach,” Journal of Intelligence and Information Systems, Vol. 22, No. 1, pp. 63-82, 2015.
Kano, N., Seraku, N., Takahashi, F., and Tsuji, S. I., “Attractive quality and must-be quality,” Journal of the Japanese Society for Quality Control, Vol. 14, No. 2, pp. 47-156, 1984.
Kim, D. K. and Kim, I. S., “An Analysis of Hotel Selection Attributes Present in Online Reviews Using Text Mining,” Journal of Tourism Sciences, Vol. 41, pp. 109-127, 2017.
Kim, E. G., “A Study on Car Reviews Using Text Mining and Association Rules,” Master dissertation, Department of Business Administration, Seoul National University of Science and Technology, 2018.
Kim, J. H. and Lee, J. M., “Comparison and Analysis of Domestic and Foreign Sports Brands Using Text Mining and Opinion Mining Analysis,” Journal of the Korea Contents Association, Vol. 18, No. 6, pp. 217-234, 2018.
Kim, J. H., Lkhagvadorj, B., and Nasridinov, A., “Construction of TF-IDF Weight- based Classifier for Sentiment Analysis of Agricultural SNS Big Data,” Proceeding of Korean Institute of Information Scientists and Engineers, pp. 283-285, 2018.
Korean Communications Agency, “Promotion Trends and Implications for Element Technology by Using Big Data,” Broadcasting Communication Technology Issue & Outlook, Vol. 10, pp. 1-6, 2013.
Lee, D. J., Won, J. U., Kwon, Y. J., and K, M. R., “A Study on Customer Satisfaction for Courier Companies based on SNS Big data,” The Journal of Society for e-Business Studies, Vol. 21, No. 4, pp. 55-67, 2016.
Lee, Y., Park, S. B., and Lee, S. J., “Aspect and Sentiment n-gram Words Extraction Using Topic Model with MaxEnt in Product Review,” Proceeding of Korean Institute of Information Scientists and Engineers, Vol. 39, No. 2B, pp. 103-105, 2012.
Liu, Y., “Word of mouth for movies: its dynamics and impact on box office revenue,” Journal of Marketing, Vol. 70, No. 3, pp. 74-89, 2006.
Lorenzo-Romero, C., Constantinides, E., and Brunink, L. A., “Co-creation: customer integration in social media based product and service development,” Procedia - Social and Behavioral Sciences, Vol. 148, pp. 383-396, 2014.
Markham, S. K., Kowolenko, M., and Michaelis, T. L., “Unstructured Text Analytics to Support New product Development Decisions,” Research-Technology Management, Vol. 58, No. 2, pp. 30-39, 2015.
Nam, K., “A Study of Sentiment Expressions in Product Reviews: An Interface between Sentiment Analysis and Korean Linguistics,” The Journal of Linguistics Science, Vol. 78, pp. 101-123, 2016.
Oh, S. W. and Jin, S. H., “A study on Analysis of Internet Shopping Mall Customers’ Reviews by Text Mining,” Journal of The Korean Data Analysis Society, Vol. 14, pp. 125-137, 2012.
Park, D. H., Lee, J., and Han, I., “The effect of on-line consumer reviews on consumer reviews on consumer purchasing intention: the moderating role of involvement,” International Journal of Electronic Commerce, Vol. 11, No. 4, pp. 125-148, 2007.
Park, H. J., Song, M. C., and Shin, K. S., “Sentiment Analysis of Korean Reviews Using CNN-Focusing on Morpheme Embedding,” Journal of Intelligence and Information Systems, Vol. 24, No. 2, pp. 59-83, 2018.
Park, Y. J. and Kim, K. J., “Impact of Semantic Characteristics on Perceived Helpfulness of Online Reviews,” Journal of Intelligence and Information Systems, Vol. 23, No. 3, pp. 29-44, 2017.
Salehan, M. and Dan, K., “Predicting the performance of online consumer reviews: A sentiment mining approach,” Decision Support Systems, Vol. 81, pp. 30-40, 2014.
Seo, J. H., Bang, G. H., and Kang, S. H., “Success factor analysis of one-person creator using sentiment analysis,” Korean Operations Research and Management Science Society, Vol. 2018, No. 04, pp. 1149-1158, 2018.
Seo, J. W., Shon, T. S., Seo, J. T., and Moon, J. S., “A study on the Filtering of Spam E-mail using n-Gram indexing and Support Vector Machine,” Journal of the Korea Institute of Information Security & Cryptology, Vol. 14, No. 2, pp. 23-33, 2004.
Shim, K. S., “Syllable-based Korean Morphological Analysis using n-grams extracted from POS Tagged Corpus,” Journal of KISS: Software and Applications, Vol. 40, No. 12, pp. 869-876, 2013.
Xu, Z., Frankwick, G. L., and Ramirez, E., “Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective,” Journal of Business Research, Vol. 69, pp. 1562-1566, 2016.
Yoo, I. J., Seo, B. G., and Park, D. H., “Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments,” Journal of Intelligence and Information Systems, Vol. 24, No. 1, pp. 25-52, 2018.
Zhu, F. and Zhang, X., “Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics,” Journal of Marketing, Vol. 74, No. 2, pp. 133-148, 2013.
Zhu, L., Yin, G., and He, W., “Is this opinion leader’s review useful? Peripheral cues for online review helpfulness,” Journal of Electronic Commerce Research, Vol. 15, pp. 267-280, 2014.
- There are currently no refbacks.