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Çevrimiçi değerlendirmelerde duyguların haritalanması: Mobil uygulamalara ilişkin kullanıcı yorumlarında duygu yelpazesinin incelenmesi

Yıl 2024, Cilt: 14 Sayı: 3, 1598 - 1619, 29.09.2024
https://doi.org/10.30783/nevsosbilen.1508802

Öz

Mobil uygulamalar, tüketiciler için güçlü platformlar olarak ortaya çıkmıştır ve mobil bağlamda kullanıcıların içerik ve duygusal yönlerine göre tutum ve tepkilerini anlamak, pazarlama karar verme sürecinde hayati önem taşımaktadır. Kapsamlı bir yaklaşıma sahip olan çalışma, belirli duyguları (öfke, tiksinti, korku, sevinç, nötr, üzüntü, şaşkınlık) inceleyerek mobil uygulamalardaki kullanıcı yorumları bağlamındaki duygu spektrumunu analiz etmeyi amaçlamaktadır. Duygu analizi metodolojisi ("Emotion English DistilRoBERTa-base" transformatör modeli aracılığıyla), 34 mobil uygulama kategorisinden 302.647 incelemeden oluşan veri kümesinde kullanılmıştır. Duyguların kategorik olarak incelenmesinde en baskın duygu kategorisinin tarafsızlık olduğu, bunu sevinç, üzüntü, tiksinme, şaşkınlık ve öfke duygu kategorilerinin takip ettiği, en az baskın olan kategorinin ise korku kategorisi olduğu görülmektedir. Polarite incelemesine göre; olumsuz kutupluk incelemeleri nötr, üzüntü ve tiksinme duygularıyla ilişkilidir; nötr kutupluk incelemeleri tarafsızlık ve üzüntüyle ilişkilendirilir; Olumlu kutupluluk incelemeleri, nötr ve sevinçli duygu kategorileriyle ilişkilidir. Analizin son kısmı duyguların tek tek incelenmesini ve her bir duygunun baskınlık sıklığı en yüksek olduğu mobil uygulama kategorilerinin sunulmasını içermektedir. Duyguların dağılım oranları ve farklı uygulama kategorileriyle duyguların bireysel ilişkileri gelecekteki akademik araştırmalara ve pazarlama karar alma süreçlerine ışık tutar.

Kaynakça

  • Alboqami, H., Al-Karaghouli, W., Baeshen, Y., Erkan, I., Evans, C., & Ghoneim, A. (2015). Electronic word of mouth in social media: the common characteristics of retweeted and favourited marketer-generated content posted on Twitter. International Journal of Internet Marketing and Advertising, 9(4), 338-358.
  • Alqahtani, F., & Orji, R. (2020). Insights from user reviews to improve mental health apps. Health informatics journal, 26(3), 2042-2066.
  • Arambepola, N., Munasinghe, L., & Warnajith, N. (2024, February). Factors Influencing Mobile App User Experience: An Analysis of Education App User Reviews. In 2024 4th International Conference on Advanced Research in Computing (ICARC) (pp. 223-228). IEEE.
  • Bai, X. (2011). Predicting consumer sentiments from online text. Decision Support Systems, 50(4), 732-742.
  • Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of interactive marketing, 15(3), 31-40.
  • Bhadane, C., Dalal, H., & Doshi, H. (2015). Sentiment analysis: Measuring opinions. Procedia Computer Science, 45, 808-814.
  • Cheung, C. M., Lee, M. K., & Rabjohn, N. (2008). The impact of electronic word‐of‐mouth: The adoption of online opinions in online customer communities. Internet research, 18(3), 229-247.
  • Çevrimkaya, M. (2023). Destinasyonlara Yönelik Sosyal Medya Paylaşımlarının Duygu Analizi. Kent Akademisi, 16(3), 1907-1929. https://doi.org/10.35674/kent.1316264
  • Ekman, P. (1992). An argument for basic emotions. Cognition & emotion, 6(3-4), 169-200.
  • Felbermayr, A., & Nanopoulos, A. (2016). The role of emotions for the perceived usefulness in online customer reviews. Journal of Interactive Marketing, 36(1), 60-76.
  • Guo, J., Wang, X., & Wu, Y. (2020). Positive emotion bias: Role of emotional content from online customer reviews in purchase decisions. Journal of Retailing and Consumer Services, 52, 101891.
  • Hartmann, J. (2022). Emotion English DistilRoBERTa-base. Retrieved from https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/
  • Herrando, C., Jiménez-Martínez, J., Martín-De Hoyos, M. J., & Constantinides, E. (2022). Emotional contagion triggered by online consumer reviews: Evidence from a neuroscience study. Journal of Retailing and Consumer Services, 67, 102973.
  • Huebner, J., Frey, R. M., Ammendola, C., Fleisch, E., & Ilic, A. (2018, November). What people like in mobile finance apps: An analysis of user reviews. In Proceedings of the 17th international conference on mobile and ubiquitous multimedia (pp. 293-304).
  • Hossain, M. S., & Rahman, M. F. (2024). Detection of readers' emotional aspects and thumbs-up empathy reactions towards reviews of online travel agency apps. Journal of Hospitality and Tourism Insights, 7(1), 142-171.
  • Hussain, S., Guangju, W., Jafar, R. M. S., Ilyas, Z., Mustafa, G., & Jianzhou, Y. (2018). Consumers' online information adoption behavior: Motives and antecedents of electronic word of mouth communications. Computers in Human Behavior, 80, 22-32.
  • Jalilvand, M. R., Esfahani, S. S., & Samiei, N. (2011). Electronic word-of-mouth: Challenges and opportunities. Procedia Computer Science, 3, 42-46.
  • Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American society for information science and technology, 60(11), 2169-2188.
  • Karakol, D. U., & Cömert, Ç. (2023). COVID-19 Aşıları için Türkçe Tweetlerle Duygu Analizi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 14(4), 639-652. https://doi.org/10.24012/dumf.1358519
  • Kim, H. Y., Kim, Y. K., Jolly, L., & Fairhurst, A. (2010). The role of love in satisfied customers' relationships with retailers. The International Review of Retail, Distribution and Consumer Research, 20(3), 285-296.
  • Khoa, B. T., & Huynh, T. T. (2022). How do customer anxiety levels impact relationship marketing in electronic commerce?. Cogent Business & Management, 9(1), 2136928.
  • Kozinets, R. V., De Valck, K., Wojnicki, A. C., & Wilner, S. J. (2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of marketing, 74(2), 71-89.
  • Langan, R., Besharat, A., & Varki, S. (2017). The effect of review valence and variance on product evaluations: An examination of intrinsic and extrinsic cues. International Journal of Research in Marketing, 34(2), 414-429. Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.
  • Lee, S. (2018). Enhancing customers’ continued mobile app use in the service industry. Journal of Services Marketing, 32(6), 680-691.
  • Liang, T. P., Li, X., Yang, C. T., & Wang, M. (2015). What in consumer reviews affects the sales of mobile apps: A multifacet sentiment analysis approach. International Journal of Electronic Commerce, 20(2), 236-260.
  • Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism management, 29(3), 458-468.
  • Liu, J., Li, C., Ji, Y. G., North, M., & Yang, F. (2017). Like it or not: The Fortune 500's Facebook strategies to generate users' electronic word-of-mouth. Computers in Human Behavior, 73, 605-613.
  • McColl-Kennedy, J. R., Patterson, P. G., Smith, A. K., & Brady, M. K. (2009). Customer rage episodes: emotions, expressions and behaviors. Journal of Retailing, 85(2), 222-237.
  • Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4), 1093-1113.
  • Mingyu, J. (2024). Google Play Scraper. GitHub repository. Retrieved from https://github.com/JoMingyu/google-play-scraper
  • Mondal, A. S., Zhu, Y., Bhagat, K. K., & Giacaman, N. (2024). Analysing user reviews of interactive educational apps: a sentiment analysis approach. Interactive Learning Environments, 32(1), 355-372.
  • Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly, 185-200.
  • Naseem, U., Razzak, I., Khan, S. K., & Prasad, M. (2021). A comprehensive survey on word representation models: From classical to state-of-the-art word representation language models. Transactions on Asian and Low-Resource Language Information Processing, 20(5), 1-35.
  • Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.
  • Nyer, P. U. (2000). An investigation into whether complaining can cause increased consumer satisfaction. Journal of consumer marketing, 17(1), 9-19.
  • Özgür, A., Sağlam, F., Burkay, G. E. N. Ç., & Altun, A. (2024). Çokluortam Öğrenme Materyalinde Duygu Salınımını Belirleme. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 1-29. https://doi.org/10.9779/pauefd.1178733
  • Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International journal of electronic commerce, 11(4), 125-148.
  • Permana, M. E., Ramadhan, H., Budi, I., Santoso, A. B., & Putra, P. K. (2020, November). Sentiment analysis and topic detection of mobile banking application review. In 2020 Fifth International Conference on Informatics and Computing (ICIC) (pp. 1-6). IEEE.
  • Pınarbaşı, F., & Canbolat, Z. N. (2018). Evaluation of augmented reality mobile applications in turkey market: A data mining approach to consumer reviews. Changing Organizations: From the Psychological and Technological Perspectives içinde, 187-197.
  • Plutchik, R. (2001). The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. American scientist, 89(4), 344-350.
  • Purnawirawan, N., Eisend, M., De Pelsmacker, P., & Dens, N. (2015). A meta-analytic investigation of the role of valence in online reviews. Journal of Interactive Marketing, 31(1), 17-27.
  • Sally, M. S. (2023). Why are consumers dissatisfied? A text mining approach on Sri Lankan mobile banking apps. International Journal of Intelligent Computing and Cybernetics, 16(4), 727-744.
  • Sensor Tower. (2024). Top Charts. Retrieved from https://app.sensortower.com/top-charts?category=all&country=US&date=2024-05-06&device=iphone&os=android
  • Van Rossum, G., & Drake Jr, F. L. (1995). Python tutorial.
  • We Are Social., & Meltwater. (2024). Digital 2024. Retrieved from https://wearesocial.com/uk/blog/2024/01/digital-2024/
  • Westbrook, R. A. (1987). Product/consumption-based affective responses and postpurchase processes. Journal of marketing research, 24(3), 258-270.
  • Yang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020). Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE access, 8, 23522-23530.
  • Zhang, C., & Laroche, M. (2020). Brand hate: a multidimensional construct. Journal of Product & Brand Management, 30(3), 392-414.

Mapping the Online Reviews Sentiment Landscape: An Exploration of Emotion Spectrum in User Reviews of Mobile Apps

Yıl 2024, Cilt: 14 Sayı: 3, 1598 - 1619, 29.09.2024
https://doi.org/10.30783/nevsosbilen.1508802

Öz

Mobile applications have emerged as powerful platforms for consumers and understanding the attitudes and reactions of users by content and emotional sides in a mobile context becomes crucial for marketing decision-making. The study with comprehensive approach aims to analyze the emotion spectrum in mobile applications user reviews context by examining specific emotions (anger, disgust, fear, joy, neutral, sadness, surprise). Sentiment analysis methodology (through "Emotion English DistilRoBERTa-base" transformers-model) is employed on the dataset of 302.647 reviews from 34 mobile application categories. Categorical examination of emotions indicates that neutrality is the dominant emotion category, followed by joy, sadness, disgust, surprise, and anger emotion categories, while the fear category is the least dominant category. According to polarity examination; negative polarity reviews are associated with neutral, sadness and disgust emotions; neutral polarity reviews are associated with neutral and sadness; positive polarity reviews are associated with neutral and joy emotion categories. Final part of analysis includes examination of emotions individually and mobile app categories which each emotion with the highest frequency of dominance are presented. The distribution rates of emotions and the individual relationships of emotions with different application categories can shed light on future academic research and marketing decision-making.

Kaynakça

  • Alboqami, H., Al-Karaghouli, W., Baeshen, Y., Erkan, I., Evans, C., & Ghoneim, A. (2015). Electronic word of mouth in social media: the common characteristics of retweeted and favourited marketer-generated content posted on Twitter. International Journal of Internet Marketing and Advertising, 9(4), 338-358.
  • Alqahtani, F., & Orji, R. (2020). Insights from user reviews to improve mental health apps. Health informatics journal, 26(3), 2042-2066.
  • Arambepola, N., Munasinghe, L., & Warnajith, N. (2024, February). Factors Influencing Mobile App User Experience: An Analysis of Education App User Reviews. In 2024 4th International Conference on Advanced Research in Computing (ICARC) (pp. 223-228). IEEE.
  • Bai, X. (2011). Predicting consumer sentiments from online text. Decision Support Systems, 50(4), 732-742.
  • Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of interactive marketing, 15(3), 31-40.
  • Bhadane, C., Dalal, H., & Doshi, H. (2015). Sentiment analysis: Measuring opinions. Procedia Computer Science, 45, 808-814.
  • Cheung, C. M., Lee, M. K., & Rabjohn, N. (2008). The impact of electronic word‐of‐mouth: The adoption of online opinions in online customer communities. Internet research, 18(3), 229-247.
  • Çevrimkaya, M. (2023). Destinasyonlara Yönelik Sosyal Medya Paylaşımlarının Duygu Analizi. Kent Akademisi, 16(3), 1907-1929. https://doi.org/10.35674/kent.1316264
  • Ekman, P. (1992). An argument for basic emotions. Cognition & emotion, 6(3-4), 169-200.
  • Felbermayr, A., & Nanopoulos, A. (2016). The role of emotions for the perceived usefulness in online customer reviews. Journal of Interactive Marketing, 36(1), 60-76.
  • Guo, J., Wang, X., & Wu, Y. (2020). Positive emotion bias: Role of emotional content from online customer reviews in purchase decisions. Journal of Retailing and Consumer Services, 52, 101891.
  • Hartmann, J. (2022). Emotion English DistilRoBERTa-base. Retrieved from https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/
  • Herrando, C., Jiménez-Martínez, J., Martín-De Hoyos, M. J., & Constantinides, E. (2022). Emotional contagion triggered by online consumer reviews: Evidence from a neuroscience study. Journal of Retailing and Consumer Services, 67, 102973.
  • Huebner, J., Frey, R. M., Ammendola, C., Fleisch, E., & Ilic, A. (2018, November). What people like in mobile finance apps: An analysis of user reviews. In Proceedings of the 17th international conference on mobile and ubiquitous multimedia (pp. 293-304).
  • Hossain, M. S., & Rahman, M. F. (2024). Detection of readers' emotional aspects and thumbs-up empathy reactions towards reviews of online travel agency apps. Journal of Hospitality and Tourism Insights, 7(1), 142-171.
  • Hussain, S., Guangju, W., Jafar, R. M. S., Ilyas, Z., Mustafa, G., & Jianzhou, Y. (2018). Consumers' online information adoption behavior: Motives and antecedents of electronic word of mouth communications. Computers in Human Behavior, 80, 22-32.
  • Jalilvand, M. R., Esfahani, S. S., & Samiei, N. (2011). Electronic word-of-mouth: Challenges and opportunities. Procedia Computer Science, 3, 42-46.
  • Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American society for information science and technology, 60(11), 2169-2188.
  • Karakol, D. U., & Cömert, Ç. (2023). COVID-19 Aşıları için Türkçe Tweetlerle Duygu Analizi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 14(4), 639-652. https://doi.org/10.24012/dumf.1358519
  • Kim, H. Y., Kim, Y. K., Jolly, L., & Fairhurst, A. (2010). The role of love in satisfied customers' relationships with retailers. The International Review of Retail, Distribution and Consumer Research, 20(3), 285-296.
  • Khoa, B. T., & Huynh, T. T. (2022). How do customer anxiety levels impact relationship marketing in electronic commerce?. Cogent Business & Management, 9(1), 2136928.
  • Kozinets, R. V., De Valck, K., Wojnicki, A. C., & Wilner, S. J. (2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of marketing, 74(2), 71-89.
  • Langan, R., Besharat, A., & Varki, S. (2017). The effect of review valence and variance on product evaluations: An examination of intrinsic and extrinsic cues. International Journal of Research in Marketing, 34(2), 414-429. Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.
  • Lee, S. (2018). Enhancing customers’ continued mobile app use in the service industry. Journal of Services Marketing, 32(6), 680-691.
  • Liang, T. P., Li, X., Yang, C. T., & Wang, M. (2015). What in consumer reviews affects the sales of mobile apps: A multifacet sentiment analysis approach. International Journal of Electronic Commerce, 20(2), 236-260.
  • Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism management, 29(3), 458-468.
  • Liu, J., Li, C., Ji, Y. G., North, M., & Yang, F. (2017). Like it or not: The Fortune 500's Facebook strategies to generate users' electronic word-of-mouth. Computers in Human Behavior, 73, 605-613.
  • McColl-Kennedy, J. R., Patterson, P. G., Smith, A. K., & Brady, M. K. (2009). Customer rage episodes: emotions, expressions and behaviors. Journal of Retailing, 85(2), 222-237.
  • Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4), 1093-1113.
  • Mingyu, J. (2024). Google Play Scraper. GitHub repository. Retrieved from https://github.com/JoMingyu/google-play-scraper
  • Mondal, A. S., Zhu, Y., Bhagat, K. K., & Giacaman, N. (2024). Analysing user reviews of interactive educational apps: a sentiment analysis approach. Interactive Learning Environments, 32(1), 355-372.
  • Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly, 185-200.
  • Naseem, U., Razzak, I., Khan, S. K., & Prasad, M. (2021). A comprehensive survey on word representation models: From classical to state-of-the-art word representation language models. Transactions on Asian and Low-Resource Language Information Processing, 20(5), 1-35.
  • Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.
  • Nyer, P. U. (2000). An investigation into whether complaining can cause increased consumer satisfaction. Journal of consumer marketing, 17(1), 9-19.
  • Özgür, A., Sağlam, F., Burkay, G. E. N. Ç., & Altun, A. (2024). Çokluortam Öğrenme Materyalinde Duygu Salınımını Belirleme. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 1-29. https://doi.org/10.9779/pauefd.1178733
  • Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International journal of electronic commerce, 11(4), 125-148.
  • Permana, M. E., Ramadhan, H., Budi, I., Santoso, A. B., & Putra, P. K. (2020, November). Sentiment analysis and topic detection of mobile banking application review. In 2020 Fifth International Conference on Informatics and Computing (ICIC) (pp. 1-6). IEEE.
  • Pınarbaşı, F., & Canbolat, Z. N. (2018). Evaluation of augmented reality mobile applications in turkey market: A data mining approach to consumer reviews. Changing Organizations: From the Psychological and Technological Perspectives içinde, 187-197.
  • Plutchik, R. (2001). The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. American scientist, 89(4), 344-350.
  • Purnawirawan, N., Eisend, M., De Pelsmacker, P., & Dens, N. (2015). A meta-analytic investigation of the role of valence in online reviews. Journal of Interactive Marketing, 31(1), 17-27.
  • Sally, M. S. (2023). Why are consumers dissatisfied? A text mining approach on Sri Lankan mobile banking apps. International Journal of Intelligent Computing and Cybernetics, 16(4), 727-744.
  • Sensor Tower. (2024). Top Charts. Retrieved from https://app.sensortower.com/top-charts?category=all&country=US&date=2024-05-06&device=iphone&os=android
  • Van Rossum, G., & Drake Jr, F. L. (1995). Python tutorial.
  • We Are Social., & Meltwater. (2024). Digital 2024. Retrieved from https://wearesocial.com/uk/blog/2024/01/digital-2024/
  • Westbrook, R. A. (1987). Product/consumption-based affective responses and postpurchase processes. Journal of marketing research, 24(3), 258-270.
  • Yang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020). Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE access, 8, 23522-23530.
  • Zhang, C., & Laroche, M. (2020). Brand hate: a multidimensional construct. Journal of Product & Brand Management, 30(3), 392-414.
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Dijital Pazarlama
Bölüm Makaleler
Yazarlar

Fatih Pınarbaşı 0000-0001-9005-0324

Erken Görünüm Tarihi 24 Eylül 2024
Yayımlanma Tarihi 29 Eylül 2024
Gönderilme Tarihi 2 Temmuz 2024
Kabul Tarihi 13 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 14 Sayı: 3

Kaynak Göster

APA Pınarbaşı, F. (2024). Mapping the Online Reviews Sentiment Landscape: An Exploration of Emotion Spectrum in User Reviews of Mobile Apps. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 14(3), 1598-1619. https://doi.org/10.30783/nevsosbilen.1508802