• Title/Summary/Keyword: Consumer sentiment

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The Analysis of Factors which Affect Business Survey Index Using Regression Trees (회귀나무를 이용한 기업경기실사지수의 영향요인 분석)

  • Chang, Young-Jae
    • The Korean Journal of Applied Statistics
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    • v.23 no.1
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    • pp.63-71
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    • 2010
  • Business entrepreneurs reflect their views of domestic and foreign economic activities on their operation for the growth of their business. The decision, forecasting, and planning based on their economic sentiment affect business operation such as production, investment, and hiring and consequently affect condition of national economy. Business survey index(BSI) is compiled to get the information of business entrepreneurs' economic sentiment for the analysis of business condition. BSI has been used as an important variable in the short-term forecasting models for business cycle analysis, especially during the the period of extreme business fluctuations. Recent financial crisis has arised extreme business fluctuations similar to those caused by currency crisis at the end of 1997, and brought back the importance of BSI as a variable for the economic forecasting. In this paper, the meaning of BSI as an economic sentiment index is reviewed and a GUIDE regression tree is constructed to find out the factors which affect on BSI. The result shows that the variables related to the stability of financial market such as kospi index(Korea composite stock price index) and exchange rate as well as manufacturing operation ratio and consumer goods sales are main factors which affect business entrepreneurs' economic sentiment.

Brand Platformization and User Sentiment: A Text Mining Analysis of Nike Run Club with Comparative Insights from Adidas Runtastic (텍스트마이닝을 활용한 브랜드 플랫폼 사용자 감성 분석: 나이키 및 아디다스 러닝 앱 리뷰 비교분석을 중심으로)

  • Hanna Park;Yunho Maeng;Hyogun Kym
    • Knowledge Management Research
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    • v.25 no.1
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    • pp.43-66
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    • 2024
  • In an era where digital technology reshapes brand-consumer interactions, this study examines the influence of Nike's Run Club and Adidas' Runtastic apps on loyalty and advocacy. Analyzing 3,715 English reviews from January 2020 to October 2023 through text mining, and conducting a focused sentiment analysis on 155 'recommend' mentions, we explore the nuances of 'hot loyalty'. The findings reveal Nike as a 'companion' with an emphasis on emotional engagement, versus Runtastic's 'tool' focus on reliability. This underscores the varied consumer perceptions across similar platforms, highlighting the necessity for brands to integrate user preferences and address technical flaws to foster loyalty. Demonstrating how customized technology adaptations impact loyalty, this research offers crucial insights for digital brand strategy, suggesting a proactive approach in app development and management for brand loyalty enhancement

Formulating Strategies from Consumer Opinion Analysis on AI Kids Phone using Text Mining (AI 키즈폰의 소비자리뷰 분석을 통한 제품개선 전략에 대한 연구)

  • Kim, Dohun;Cha, Kyungjin
    • The Journal of Society for e-Business Studies
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    • v.24 no.2
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    • pp.71-89
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    • 2019
  • 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/$Caf\acute{e}$. 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.

Wireless Earphone Consumers Using LDA Topic Modeling Comparative Analysis of Purchase Intention and Satisfaction: Focused on Samsung and Apple wireless earphone reviews in Coupang (LDA 토픽 모델링을 활용한 무선이어폰 소비자 구매 의도 및 만족도 비교 분석: 쿠팡에서의 삼성과 애플 무선이어폰 리뷰를 중심으로)

  • Tuul Yondon;Tae-Gu Kang
    • Journal of Industrial Convergence
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    • v.21 no.8
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    • pp.23-33
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    • 2023
  • Consumer review analysis is important for product development, customer satisfaction, competitive advantage, and effective marketing. Increased use of wireless earphones is expected to reach $45.7 billion by 2026 with growth in lifestyle. Therefore, in consideration of the growth and importance of the market, consumer reviews of wireless earphones from Apple and Samsung were analyzed. In this study, 11,320 wireless earphone reviews from Apple and Samsung sold on Coupang were collected to analyze consumers' purchase intentions and analyze consumer satisfaction through analysis of the frequency, sensitivity, and LDA topic model of text mining. As a result of topic modeling, 16 topics were derived and classified into sound quality, connection, shopping mall service, purchase intention, battery, delivery, and price. As a result of brand comparison, Samsung purchased a lot for gift purposes, had a high positive sentiment for price, and Apple had a high positive sentiment for battery, sound quality, connection, service, and delivery. The results of this study can be used as data for related industries as a result of research that can obtain improvements and insights on customer satisfaction, quality and market trends, including manufacturing, retail, marketers, and consumers.

Consumers' perceptions of dietary supplements before and after the COVID-19 pandemic based on big data

  • Eunjung Lee;Hyo Sun Jung;Jin A Jang
    • Journal of Nutrition and Health
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    • v.56 no.3
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    • pp.330-347
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    • 2023
  • Purpose: This study identified words closely associated with the keyword "dietary supplement" (DS) using big data in Korean social media and investigated consumer perceptions and trends related to DSs before (2019) and after the coronavirus disease 2019 (COVID-19) pandemic (2021). Methods: A total of 37,313 keywords were found for the 2019 period, and 35,336 keywords were found for the 2021 period using blogs and cafes on Daum and Naver. Results were derived by text mining, semantic networking, network visualization analysis, and sentiment analysis. Results: The DS-related keywords that frequently appeared before and after COVID-19 were "recommend", "vitamin", "health", "children", "multiple", and "lactobacillus". "Calcium", "lutein", "skin", and "immunity" also had high frequency-inverse document frequency (TF-IDF) values. These keywords imply a keen interest in DSs among Korean consumers. Big data results also reflected social phenomena related to DSs; for example, "baby" and "pregnant woman" had lower TD-IDF values after the pandemic, suggesting lower marriage and birth rates but higher values for "joint", indicating reduced physical activity. A network centered on vitamins and health care was produced by semantic network analysis in 2019. In 2021, values were highest for deficiency and need, indicating that individuals were searching for DSs after the COVID-19 pandemic due to a lack an awareness of the need for adequate nutrient intake. Before the pandemic, DSs and vitamins were associated with healthcare and life cycle-related topics, such as pregnancy, but after the COVID-19 pandemic, consumer interests changed to disease prevention and treatment. Conclusion: This study provides meaningful clues regarding consumer perceptions and trends related to DSs before and after the COVID-19 pandemic and fundamental data on the effect of the pandemic on consumer interest in dietary supplements.

Customer Value Proposition Methodology Using Text Mining of Online Customer Reviews (온라인 고객 리뷰에 대한 텍스트마이닝을 활용한 고객가치제안 방법)

  • Han, Young-Kyung;Kim, Chul-Min;Park, Kwang-Ho
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.44 no.4
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    • pp.85-97
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    • 2021
  • Online consumer activities have increased considerably since the COVID-19 outbreak. For the products and services which have an impact on everyday life, online reviews and recommendations can play a significant role in consumer decision-making processes. Thus, to better serve their customers, online firms are required to build online-centric marketing strategies. Especially, it is essential to define core value of customers based on the online customer reviews and to propose these values to their customers. This study discovers specific perceived values of customers in regard to a certain product and service, using online customer reviews and proposes a customer value proposition methodology which enables online firms to develop more effective marketing strategies. In order to discover customers value, the methodology employs a text-mining technology, which combines a sentiment analysis and topic modeling. By the methodology, customer emotions and value factors can be more clearly defined. It is expected that online firms can better identify value elements of their respective customers, provide appropriate value propositions, and thus gain sustainable competitive advantage.

The Impact of Online Reviews on Hotel Ratings through the Lens of Elaboration Likelihood Model: A Text Mining Approach

  • Qiannan Guo;Jinzhe Yan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.10
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    • pp.2609-2626
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    • 2023
  • The hotel industry is an example of experiential services. As consumers cannot fully evaluate the online review content and quality of their services before booking, they must rely on several online reviews to reduce their perceived risks. However, individuals face information overload owing to the explosion of online reviews. Therefore, consumer cognitive fluency is an individual's subjective experience of the difficulty in processing information. Information complexity influences the receiver's attitude, behavior, and purchase decisions. Individuals who cannot process complex information rely on the peripheral route, whereas those who can process more information prefer the central route. This study further discusses the influence of the complexity of review information on hotel ratings using online attraction review data retrieved from TripAdvisor.com. This study conducts a two-level empirical analysis to explore the factors that affect review value. First, in the Peripheral Route model, we introduce a negative binomial regression model to examine the impact of intuitive and straightforward information on hotel ratings. In the Central Route model, we use a Tobit regression model with expert reviews as moderator variables to analyze the impact of complex information on hotel ratings. According to the analysis, five-star and budget hotels have different effects on hotel ratings. These findings have immediate implications for hotel managers in terms of better identifying potentially valuable reviews.

Nation branding and international media coverage of domestic conflict: An agenda-setting study

  • Jami Fullerton;John P. Schoeneman Jr;Alice Kendrick
    • Journal of Public Diplomacy
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    • v.3 no.2
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    • pp.46-55
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    • 2023
  • Agenda-setting theory of the media describes the relationships between how news media present issues and how important those issues are to news consumers. Applying agenda setting theory, we ask, does media coverage of international political events relate to a nation's brand? We leverage a domestic conflict index variable from the Cross National Time-Series (CNTS) dataset of New York Times articles, which are considered influencers of global news content. Our dependent variable is Anholt's NBI index from consumer surveys from 2005 through 2007, whose data include an aggregate nation brand index score as well as a disaggregated score for different categories such as tourism and exports. To investigate these relationships, we employ general additive models (GAM), which account for non-linearity using cubic splines, finding generally a negative relationship between media coverage of conflict and consumer sentiment of a country. To confirm our findings, we compare the results with both the Global Database of Events, Language and Tone (GDELT) dataset of political events and the Upsala Conflict Data Program (UCDP) measure of reported conflict fatalities in a country.

Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments (Smart Store in Smart City: 소비자 감성기반 상권분석 시스템 개발)

  • Yoo, In-Jin;Seo, Bong-Goon;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.24 no.1
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    • pp.25-52
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    • 2018
  • This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers' web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area's public data to verify factors affecting the area's sales. According to R square's change, We can see that the model has a little high R square value even though it includes only the district's public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area's public data showed that the five factors of 'number of market district,' 'residential area per person,' 'satisfaction of residential environment,' 'rate of change of trade,' and 'survival rate over 3 years' among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, 'residential area per person' has the highest standardized beta value. Therefore, 'residential area per person' has the strongest influence on commercial sales. In addition, 'residential area per person,' 'number of market district,' and 'survival rate over 3 years' were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, 'satisfaction of residential environment' and 'rate of change of trade' were found to have a negative effect on sales. In the case of 'satisfaction of residential environment,' sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The 'rate of change of trade' shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are 'density of ego network,' 'degree centrality,' 'closeness centrality,' 'betweenness centrality,' and 'eigenvector centrality.' The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. 'Degree centrality' has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low 'degree centrality,' it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, 'eigenvector centrality' has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers' desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure.

Sentiment lexicon modeling for consumer analysis (소비자 분석을 위한 감성사전 모델링)

  • Lee, Jae-Woong;Yun, Hyun-Noh;Moon, Nammee
    • Annual Conference of KIPS
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    • 2017.04a
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    • pp.850-853
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    • 2017
  • 본 논문은, 크롤링을 통해 얻은 비정형 데이터를 'Python'의 'KoNLPy' 라이브러리를 사용해 형태소 분석한 후 텍스트 마이닝을 통한 감성사전 구축을 목표로 하고 있으며, 형태소들의 빈도수를 기반으로 가중치로 두어 선별된 단어들을 이용해 긍정과 부정으로 나누어 카테고리화 한다. 이후, 선별한 카테고리에 단어의 극성을 판단하여 감성사전을 모델링한다. 실험을 위하여, 온라인 쇼핑몰 리뷰를 크롤링하여 비정형 데이터를 수집하고, 수집한 데이터를 분석, 가공 과정을 거쳐 정형화된 단어를 추출한다. 그 후에, 리뷰에 자주 사용되는 단어를 바탕으로 카테고리를 구성하였다. 구성된 카테고리 별로 단어의 극성을 판단하여 소비자 성향을 분석한 결과, 단순히 긍정과 부정을 표현하는 범용 감성사전보다 더 세분화된 감성 사전을 구축 할 수 있었다.