Araştırma Makalesi
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Sosyal Medya Analitiğinde Makine Öğrenmesi Uygulamaları: Literatür İncelemesi

Yıl 2021, Cilt 16, Sayı 61, 95 - 127, 31.01.2021
https://doi.org/10.19168/jyasar.687093

Öz

Sosyal medya platformlarından kullanıcı tarafından oluşturulan verilerin toplanması ve analiz edilmesini ifade eden sosyal medya analitiği (SMA), tüketici içgörüleri elde etmeye çalışan araştırmacıların ve uygulayıcıların ilgi odağındadır. Bu alan, makine öğrenimi algoritmalarının yüksek hacimli ve karmaşık verileri uygun maliyetli bir şekilde işleyerek kayda değer içgörüleri yakalama kapasitesine paralel olarak çok yönlü bir şekilde büyümesini sürdürmektedir. Makine öğrenimi uygulamaları, sosyal medya analitiğinin geleceğini yeniden şekillendirebilecek verimli bir alan olarak dikkat çektiğinden, mevcut trendleri ve yaklaşımları bütünleştirici bir çerçevede anlamaya ihtiyaç vardır. Bu bağlamda, mevcut çalışma sosyal medya analitiği alanındaki makine öğrenimi uygulama trendlerini ve yaklaşımlarını bütünleştirici bir çerçevede sunmayı amaçlamaktadır. 2013-2019 yılları arasında hakemli bilimsel dergilerde yer alan ve işletme, yönetim ve bilgisayar bilimleri alanında yayınlanan 42 bilimsel makale, görsel metin madenciliğine dayalı sistematik literatür taraması yöntemi ile analiz edilmiştir. Sonuçlar beş farklı araştırma kümesini ortaya çıkarmıştır: (1) inceleme siteleri, (2) mikrobloglar, (3) sosyal ağ siteleri, (4) içerik toplulukları, (5) platformlar arası çalışmalar. Mevcut çalışma, alanın entelektüel yapısı hakkındaki anlayışı geliştirmek, alanın önde gelen çalışmalarına dikkat çekmek, gelecekteki araştırmaların daha iyi konumlandırılmasına yönelik olarak alandaki boşlukları ve yeni araştırma alanlarını belirlemek açısından önemli bir rol oynamaktadır.

Kaynakça

  • Adamopoulos, P., Ghose, A., & Todri, V. (2018). The impact of user personality traits on word of mouth: Text-mining social media platforms. Information Systems Research, 29(3), 612-640.
  • Agnihotri, A., & Bhattacharya, S. (2016). Online review helpfulness: Role of qualitative factors. Psychology & Marketing, 33(11), 1006-1017.
  • Ali, F., Kwak, K. S., & Kim, Y. G. (2016). Opinion mining based on fuzzy domain ontology and Support Vector Machine: A proposal to automate online review classification. Applied Soft Computing, 47, 235-250.
  • Beyer, M. A., & Laney, D. (2012). The importance of ‘big data’: a definition. Stamford, CT: Gartner, 2014-2018.
  • Bigne, E., Oltra, E., & Andreu, L. (2019). Harnessing stakeholder input on Twitter: A case study of short breaks in Spanish tourist cities. Tourism Management, 71, 490-503.
  • Bilro, R. G., Loureiro, S. M. C., & Guerreiro, J. (2019). Exploring online customer engagement with hospitality products and its relationship with involvement, emotional states, experience and brand advocacy. Journal of Hospitality Marketing & Management, 28(2), 147-171.
  • Calheiros, A. C., Moro, S., & Rita, P. (2017). Sentiment classification of consumer-generated online reviews using topic modeling. Journal of Hospitality Marketing & Management, 26(7), 675-693.
  • Chaffey, D. (2019). Global Social Media Research Summary 2019. Available at: https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/ Accessed 10 July 2019.
  • Costa, A., Guerreiro, J., Moro, S., & Henriques, R. (2019). Unfolding the characteristics of incentivized online reviews. Journal of Retailing and Consumer Services, 47, 272-281.
  • D’Avanzo, E., Pilato, G., & Lytras, M. (2017). Using Twitter sentiment and emotions analysis of Google Trends for decisions making. Program, 51(3), 322-350.
  • Demirci, S., & Sağıroğlu, Ş. (2017). Sosyal Ağ Verilerinin Kullanım Alanları Üzerine Kapsamlı Bir İnceleme. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(2), 1-21.
  • Dhaoui, C., Webster, C. M., & Tan, L. P. (2017). Social media sentiment analysis: lexicon versus machine learning. Journal of Consumer Marketing, 34(6), 480-488.
  • Eslami, S. P., & Ghasemaghaei, M. (2018). Effects of online review positiveness and review score inconsistency on sales: A comparison by product involvement. Journal of Retailing and Consumer Services, 45, 74-80.
  • Fabbri, S., Hernandes, E., Di Thommazo, A., Belgamo, A., Zamboni, A., & Silva, C. (2012, June). Using information visualization and text mining to facilitate the conduction of systematic literature reviews. In International Conference on Enterprise Information Systems, pp. 243-256. Springer, Berlin, Heidelberg.
  • Felizardo, K. R., Salleh, N., Martins, R. M., Mendes, E., MacDonell, S. G., & Maldonado, J. C. (2011, September). Using visual text mining to support the study selection activity in systematic literature reviews. In 2011 International Symposium on Empirical Software Engineering and Measurement, pp. 77-86.
  • Ghiassi, M., Skinner, J., & Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with applications, 40(16), 6266-6282.
  • Giglio, S., Bertacchini, F., Bilotta, E., & Pantano, P. (2019). Using social media to identify tourism attractiveness in six Italian cities. Tourism Management, 72, 306-312.
  • Greenwood, B. N., & Gopal, A. (2015). Research note—Tigerblood: Newspapers, blogs, and the founding of information technology firms. Information Systems Research, 26(4), 812–828.
  • Gurzki, H., & Woisetschläger, D. M. (2017). Mapping the luxury research landscape: A bibliometric citation analysis. Journal of Business Research, 77, 147-166.
  • Harzing, A. W. (2013). Document categories in the ISI Web of Knowledge: Misunderstanding the social sciences?. Scientometrics, 94(1), 23-34.
  • Hawkins, J. B., Brownstein, J. S., Tuli, G., Runels, T., Broecker, K., Nsoesie, E. O., ... & Greaves, F. (2016). Measuring patient-perceived quality of care in US hospitals using Twitter. BMJ Qual Saf, 25(6), 404-413.
  • Heng, Y., Gao, Z., Jiang, Y., & Chen, X. (2018). Exploring hidden factors behind online food shopping from Amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services, 42, 161-168.
  • Hu, Y. H., Shiau, W. M., Shih, S. P., & Chen, C. J. (2018). Considering online consumer reviews to predict movie box-office performance between the years 2009 and 2014 in the US. The Electronic Library, 36(6), 1010-1026.
  • Ikeda, K., Hattori, G., Ono, C., Asoh, H., & Higashino, T. (2013). Twitter user profiling based on text and community mining for market analysis. Knowledge-Based Systems, 51, 35-47.
  • Ilhan, B. E., Kübler, R. V., & Pauwels, K. H. (2018). Battle of the brand fans: impact of brand attack and defense on social media. Journal of Interactive Marketing, 43, 33-51.
  • Jang, H. J., Sim, J., Lee, Y., & Kwon, O. (2013). Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media. Expert Systems with applications, 40(18), 7492-7503.
  • Jimenez-Marquez, J. L., Gonzalez-Carrasco, I., Lopez-Cuadrado, J. L., & Ruiz-Mezcua, B. (2019). Towards a big data framework for analyzing social media content. International Journal of Information Management, 44, 1-12.
  • Kapoor, K.K., Tamilmani, K., Rana, N.P., Patil, P., Dwivedi, Y.K., Nerur, S., 2017. Advances in social media research: past, present and future. Inform. Syst. Front.
  • Kim, Y., Dwivedi, R., Zhang, J., & Jeong, S. R. (2016). Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5. Online Information Review, 40(1), 42-61.
  • Klostermann, J., Plumeyer, A., Böger, D., & Decker, R. (2018). Extracting brand information from social networks: Integrating image, text, and social tagging data. International Journal of Research in Marketing, 35(4), 538-556.
  • Kwok, L., & Yu, B. (2013). Spreading social media messages on Facebook: An analysis of restaurant business-to-consumer communications. Cornell Hospitality Quarterly, 54(1), 84-94.
  • Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: evidence from Facebook. Management Science, 64(11), 5105-5131.
  • Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363-388.
  • Meredith, J. (1993), “Theory building through conceptual methods”, International Journal of Operations & Production Management, Vol. 13 No. 5, pp. 3-11.
  • Mergel, G. D., Silveira, M. S., & da Silva, T. S. (2015, April). A method to support search string building in systematic literature reviews through visual text mining. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 1594-1601.
  • Moro, S., Rita, P., & Vala, B. (2016). Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research, 69(9), 3341-3351.
  • Nave, M., Rita, P., & Guerreiro, J. (2018). A decision support system framework to track consumer sentiments in social media. Journal of Hospitality Marketing & Management, 27(6), 693-710.
  • Nguyen, M. T., Tran, D. V., & Nguyen, L. M. (2018). Social context summarization using user-generated content and third-party sources. Knowledge-Based Systems, 144, 51-64.
  • Nilashi, M., Ibrahim, O., Yadegaridehkordi, E., Samad, S., Akbari, E., & Alizadeh, A. (2018). Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal of computational science, 28, 168-179.
  • Park, S. B., Kim, H. J., & Ok, C. M. (2018). Linking emotion and place on Twitter at Disneyland. Journal of Travel & Tourism Marketing, 35(5), 664-677.
  • Peláez, J. I., Martínez, E. A., & Vargas, L. G. (2019). Decision making in social media with consistent data. Knowledge-Based Systems, 172, 33-41.
  • Piñeiro‐Chousa, J., Vizcaíno‐González, M., & Pérez‐Pico, A. M. (2017). Influence of social media over the stock market. Psychology & Marketing, 34(1), 101-108.
  • Porter, A., Kongthon, A., & Lu, J. C. (2002). Research profiling: Improving the literature review. Scientometrics, 53(3), 351-370.
  • Pournarakis, D. E., Sotiropoulos, D. N., & Giaglis, G. M. (2017). A computational model for mining consumer perceptions in social media. Decision Support Systems, 93, 98-110.
  • Randhawa, K., Wilden, R., & Hohberger, J. (2016). A bibliometric review of open innovation: Setting a research agenda. Journal of Product Innovation Management, 33(6), 750-772.
  • Rathan, M., Hulipalled, V. R., Venugopal, K. R., & Patnaik, L. M. (2018). Consumer insight mining: Aspect based twitter opinion mining of mobile phone reviews. Applied Soft Computing, 68, 765-773.
  • Rogers, A., Daunt, K. L., Morgan, P., & Beynon, M. (2017). Examining the existence of double jeopardy and negative double jeopardy within Twitter. European Journal of Marketing, 51(7/8), 1224-1247.
  • Ryoo, J., & Bendle, N. (2017). Understanding the social media strategies of US primary candidates. Journal of Political Marketing, 16(3-4), 244-266.
  • Schneier, B. (2010). A taxonomy of social networking data. IEEE Security & Privacy, 8(4), 88-88.
  • Schniederjans, D., Cao, E. S., & Schniederjans, M. (2013). Enhancing financial performance with social media: An impression management perspective. Decision Support Systems, 55(4), 911-918.
  • Shareef, M. A., Mukerji, B., Alryalat, M. A. A., Wright, A., & Dwivedi, Y. K. (2018). Advertisements on Facebook: Identifying the persuasive elements in the development of positive attitudes in consumers. Journal of Retailing and Consumer Services, 43, 258-268.
  • Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International journal of information management, 39, 156-168.
  • Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117-126.
  • Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfr
  • Vázquez, S., Muñoz-García, Ó., Campanella, I., Poch, M., Fisas, B., Bel, N., & Andreu, G. (2014). A classification of user-generated content into consumer decision journey stages. Neural Networks, 58, 68-81.
  • Walker, L., Baines, P. R., Dimitriu, R., & Macdonald, E. K. (2017). Antecedents of retweeting in a (political) marketing context. Psychology & Marketing, 34(3), 275-293.
  • Wang, X., Baesens, B., & Zhu, Z. (2019). On the optimal marketing aggressiveness level of C2C sellers in social media: Evidence from china. Omega, 85, 83-93.
  • Wang, Y.,Wang, S., Tang, J., Liu, H., & Li, B. (2015). Unsupervised sentiment analysis for social media images (pp. 2378–2379). Proceedings of the 24th International Joint Conference on Artificial Intelligence.
  • Wong, T. C., Chan, H. K., & Lacka, E. (2017). An ANN-based approach of interpreting user-generated comments from social media. Applied Soft Computing, 52, 1169-1180.
  • Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65.

Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis

Yıl 2021, Cilt 16, Sayı 61, 95 - 127, 31.01.2021
https://doi.org/10.19168/jyasar.687093

Öz

Social media analytics (SMA), referring to the collection and analysis of user generated data from social media platforms, attract attention of both researchers and practitioners striving to derive consumer insights. The SMA domain grows multifariously, with a highlight on the capability of machine learning algorithms in capturing noteworthy insights through processing high-volume and complex data in a cost effective way. As machine learning applications draw attention as a fertile area that may re-shape the future of SMA, there is a need to comprehend trends and approaches in an integrative framework. Accordingly, this study aims to present an integrative framework by portraying machine learning application trends and approaches in SMA. 42 scientific articles published in refereed scientific business, management, and computational science journals between the years 2013 and 2019 are analyzed via systematic literature review based on visual text mining method (SLR-VTM). The results revealed five distinctive research clusters as: (1) review sites, (2) microblogs, (3) social networking sites, (4) content communities, (5) cross-media. This analysis plays a crucial role for enhancing our understanding regarding the intellectual structure of the field, acknowledging the leading studies of the domain, better positioning future research, and determining gaps and new paths for researchers.

Kaynakça

  • Adamopoulos, P., Ghose, A., & Todri, V. (2018). The impact of user personality traits on word of mouth: Text-mining social media platforms. Information Systems Research, 29(3), 612-640.
  • Agnihotri, A., & Bhattacharya, S. (2016). Online review helpfulness: Role of qualitative factors. Psychology & Marketing, 33(11), 1006-1017.
  • Ali, F., Kwak, K. S., & Kim, Y. G. (2016). Opinion mining based on fuzzy domain ontology and Support Vector Machine: A proposal to automate online review classification. Applied Soft Computing, 47, 235-250.
  • Beyer, M. A., & Laney, D. (2012). The importance of ‘big data’: a definition. Stamford, CT: Gartner, 2014-2018.
  • Bigne, E., Oltra, E., & Andreu, L. (2019). Harnessing stakeholder input on Twitter: A case study of short breaks in Spanish tourist cities. Tourism Management, 71, 490-503.
  • Bilro, R. G., Loureiro, S. M. C., & Guerreiro, J. (2019). Exploring online customer engagement with hospitality products and its relationship with involvement, emotional states, experience and brand advocacy. Journal of Hospitality Marketing & Management, 28(2), 147-171.
  • Calheiros, A. C., Moro, S., & Rita, P. (2017). Sentiment classification of consumer-generated online reviews using topic modeling. Journal of Hospitality Marketing & Management, 26(7), 675-693.
  • Chaffey, D. (2019). Global Social Media Research Summary 2019. Available at: https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/ Accessed 10 July 2019.
  • Costa, A., Guerreiro, J., Moro, S., & Henriques, R. (2019). Unfolding the characteristics of incentivized online reviews. Journal of Retailing and Consumer Services, 47, 272-281.
  • D’Avanzo, E., Pilato, G., & Lytras, M. (2017). Using Twitter sentiment and emotions analysis of Google Trends for decisions making. Program, 51(3), 322-350.
  • Demirci, S., & Sağıroğlu, Ş. (2017). Sosyal Ağ Verilerinin Kullanım Alanları Üzerine Kapsamlı Bir İnceleme. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(2), 1-21.
  • Dhaoui, C., Webster, C. M., & Tan, L. P. (2017). Social media sentiment analysis: lexicon versus machine learning. Journal of Consumer Marketing, 34(6), 480-488.
  • Eslami, S. P., & Ghasemaghaei, M. (2018). Effects of online review positiveness and review score inconsistency on sales: A comparison by product involvement. Journal of Retailing and Consumer Services, 45, 74-80.
  • Fabbri, S., Hernandes, E., Di Thommazo, A., Belgamo, A., Zamboni, A., & Silva, C. (2012, June). Using information visualization and text mining to facilitate the conduction of systematic literature reviews. In International Conference on Enterprise Information Systems, pp. 243-256. Springer, Berlin, Heidelberg.
  • Felizardo, K. R., Salleh, N., Martins, R. M., Mendes, E., MacDonell, S. G., & Maldonado, J. C. (2011, September). Using visual text mining to support the study selection activity in systematic literature reviews. In 2011 International Symposium on Empirical Software Engineering and Measurement, pp. 77-86.
  • Ghiassi, M., Skinner, J., & Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with applications, 40(16), 6266-6282.
  • Giglio, S., Bertacchini, F., Bilotta, E., & Pantano, P. (2019). Using social media to identify tourism attractiveness in six Italian cities. Tourism Management, 72, 306-312.
  • Greenwood, B. N., & Gopal, A. (2015). Research note—Tigerblood: Newspapers, blogs, and the founding of information technology firms. Information Systems Research, 26(4), 812–828.
  • Gurzki, H., & Woisetschläger, D. M. (2017). Mapping the luxury research landscape: A bibliometric citation analysis. Journal of Business Research, 77, 147-166.
  • Harzing, A. W. (2013). Document categories in the ISI Web of Knowledge: Misunderstanding the social sciences?. Scientometrics, 94(1), 23-34.
  • Hawkins, J. B., Brownstein, J. S., Tuli, G., Runels, T., Broecker, K., Nsoesie, E. O., ... & Greaves, F. (2016). Measuring patient-perceived quality of care in US hospitals using Twitter. BMJ Qual Saf, 25(6), 404-413.
  • Heng, Y., Gao, Z., Jiang, Y., & Chen, X. (2018). Exploring hidden factors behind online food shopping from Amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services, 42, 161-168.
  • Hu, Y. H., Shiau, W. M., Shih, S. P., & Chen, C. J. (2018). Considering online consumer reviews to predict movie box-office performance between the years 2009 and 2014 in the US. The Electronic Library, 36(6), 1010-1026.
  • Ikeda, K., Hattori, G., Ono, C., Asoh, H., & Higashino, T. (2013). Twitter user profiling based on text and community mining for market analysis. Knowledge-Based Systems, 51, 35-47.
  • Ilhan, B. E., Kübler, R. V., & Pauwels, K. H. (2018). Battle of the brand fans: impact of brand attack and defense on social media. Journal of Interactive Marketing, 43, 33-51.
  • Jang, H. J., Sim, J., Lee, Y., & Kwon, O. (2013). Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media. Expert Systems with applications, 40(18), 7492-7503.
  • Jimenez-Marquez, J. L., Gonzalez-Carrasco, I., Lopez-Cuadrado, J. L., & Ruiz-Mezcua, B. (2019). Towards a big data framework for analyzing social media content. International Journal of Information Management, 44, 1-12.
  • Kapoor, K.K., Tamilmani, K., Rana, N.P., Patil, P., Dwivedi, Y.K., Nerur, S., 2017. Advances in social media research: past, present and future. Inform. Syst. Front.
  • Kim, Y., Dwivedi, R., Zhang, J., & Jeong, S. R. (2016). Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5. Online Information Review, 40(1), 42-61.
  • Klostermann, J., Plumeyer, A., Böger, D., & Decker, R. (2018). Extracting brand information from social networks: Integrating image, text, and social tagging data. International Journal of Research in Marketing, 35(4), 538-556.
  • Kwok, L., & Yu, B. (2013). Spreading social media messages on Facebook: An analysis of restaurant business-to-consumer communications. Cornell Hospitality Quarterly, 54(1), 84-94.
  • Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: evidence from Facebook. Management Science, 64(11), 5105-5131.
  • Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363-388.
  • Meredith, J. (1993), “Theory building through conceptual methods”, International Journal of Operations & Production Management, Vol. 13 No. 5, pp. 3-11.
  • Mergel, G. D., Silveira, M. S., & da Silva, T. S. (2015, April). A method to support search string building in systematic literature reviews through visual text mining. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 1594-1601.
  • Moro, S., Rita, P., & Vala, B. (2016). Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research, 69(9), 3341-3351.
  • Nave, M., Rita, P., & Guerreiro, J. (2018). A decision support system framework to track consumer sentiments in social media. Journal of Hospitality Marketing & Management, 27(6), 693-710.
  • Nguyen, M. T., Tran, D. V., & Nguyen, L. M. (2018). Social context summarization using user-generated content and third-party sources. Knowledge-Based Systems, 144, 51-64.
  • Nilashi, M., Ibrahim, O., Yadegaridehkordi, E., Samad, S., Akbari, E., & Alizadeh, A. (2018). Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal of computational science, 28, 168-179.
  • Park, S. B., Kim, H. J., & Ok, C. M. (2018). Linking emotion and place on Twitter at Disneyland. Journal of Travel & Tourism Marketing, 35(5), 664-677.
  • Peláez, J. I., Martínez, E. A., & Vargas, L. G. (2019). Decision making in social media with consistent data. Knowledge-Based Systems, 172, 33-41.
  • Piñeiro‐Chousa, J., Vizcaíno‐González, M., & Pérez‐Pico, A. M. (2017). Influence of social media over the stock market. Psychology & Marketing, 34(1), 101-108.
  • Porter, A., Kongthon, A., & Lu, J. C. (2002). Research profiling: Improving the literature review. Scientometrics, 53(3), 351-370.
  • Pournarakis, D. E., Sotiropoulos, D. N., & Giaglis, G. M. (2017). A computational model for mining consumer perceptions in social media. Decision Support Systems, 93, 98-110.
  • Randhawa, K., Wilden, R., & Hohberger, J. (2016). A bibliometric review of open innovation: Setting a research agenda. Journal of Product Innovation Management, 33(6), 750-772.
  • Rathan, M., Hulipalled, V. R., Venugopal, K. R., & Patnaik, L. M. (2018). Consumer insight mining: Aspect based twitter opinion mining of mobile phone reviews. Applied Soft Computing, 68, 765-773.
  • Rogers, A., Daunt, K. L., Morgan, P., & Beynon, M. (2017). Examining the existence of double jeopardy and negative double jeopardy within Twitter. European Journal of Marketing, 51(7/8), 1224-1247.
  • Ryoo, J., & Bendle, N. (2017). Understanding the social media strategies of US primary candidates. Journal of Political Marketing, 16(3-4), 244-266.
  • Schneier, B. (2010). A taxonomy of social networking data. IEEE Security & Privacy, 8(4), 88-88.
  • Schniederjans, D., Cao, E. S., & Schniederjans, M. (2013). Enhancing financial performance with social media: An impression management perspective. Decision Support Systems, 55(4), 911-918.
  • Shareef, M. A., Mukerji, B., Alryalat, M. A. A., Wright, A., & Dwivedi, Y. K. (2018). Advertisements on Facebook: Identifying the persuasive elements in the development of positive attitudes in consumers. Journal of Retailing and Consumer Services, 43, 258-268.
  • Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International journal of information management, 39, 156-168.
  • Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117-126.
  • Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfr
  • Vázquez, S., Muñoz-García, Ó., Campanella, I., Poch, M., Fisas, B., Bel, N., & Andreu, G. (2014). A classification of user-generated content into consumer decision journey stages. Neural Networks, 58, 68-81.
  • Walker, L., Baines, P. R., Dimitriu, R., & Macdonald, E. K. (2017). Antecedents of retweeting in a (political) marketing context. Psychology & Marketing, 34(3), 275-293.
  • Wang, X., Baesens, B., & Zhu, Z. (2019). On the optimal marketing aggressiveness level of C2C sellers in social media: Evidence from china. Omega, 85, 83-93.
  • Wang, Y.,Wang, S., Tang, J., Liu, H., & Li, B. (2015). Unsupervised sentiment analysis for social media images (pp. 2378–2379). Proceedings of the 24th International Joint Conference on Artificial Intelligence.
  • Wong, T. C., Chan, H. K., & Lacka, E. (2017). An ANN-based approach of interpreting user-generated comments from social media. Applied Soft Computing, 52, 1169-1180.
  • Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65.

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Yazarlar

Birce DOBRUCALI> (Sorumlu Yazar)
İZMİR EKONOMİ ÜNİVERSİTESİ
0000-0003-3462-0606
Türkiye


Burcu İLTER>
DOKUZ EYLUL UNIVERSITY
0000-0002-3781-7263
Türkiye

Yayımlanma Tarihi 31 Ocak 2021
Yayınlandığı Sayı Yıl 2021, Cilt 16, Sayı 61

Kaynak Göster

Bibtex @araştırma makalesi { jyasar687093, journal = {Yaşar Üniversitesi E-Dergisi}, issn = {1305-970X}, address = {}, publisher = {Yaşar Üniversitesi}, year = {2021}, volume = {16}, number = {61}, pages = {95 - 127}, doi = {10.19168/jyasar.687093}, title = {Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis}, key = {cite}, author = {Dobrucalı, Birce and İlter, Burcu} }
APA Dobrucalı, B. & İlter, B. (2021). Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis . Yaşar Üniversitesi E-Dergisi , 16 (61) , 95-127 . DOI: 10.19168/jyasar.687093
MLA Dobrucalı, B. , İlter, B. "Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis" . Yaşar Üniversitesi E-Dergisi 16 (2021 ): 95-127 <https://dergipark.org.tr/tr/pub/jyasar/issue/60163/687093>
Chicago Dobrucalı, B. , İlter, B. "Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis". Yaşar Üniversitesi E-Dergisi 16 (2021 ): 95-127
RIS TY - JOUR T1 - Sosyal Medya Analitiğinde Makine Öğrenmesi Uygulamaları: Literatür İncelemesi AU - BirceDobrucalı, Burcuİlter Y1 - 2021 PY - 2021 N1 - doi: 10.19168/jyasar.687093 DO - 10.19168/jyasar.687093 T2 - Yaşar Üniversitesi E-Dergisi JF - Journal JO - JOR SP - 95 EP - 127 VL - 16 IS - 61 SN - 1305-970X- M3 - doi: 10.19168/jyasar.687093 UR - https://doi.org/10.19168/jyasar.687093 Y2 - 2020 ER -
EndNote %0 Yaşar Üniversitesi E-Dergisi Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis %A Birce Dobrucalı , Burcu İlter %T Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis %D 2021 %J Yaşar Üniversitesi E-Dergisi %P 1305-970X- %V 16 %N 61 %R doi: 10.19168/jyasar.687093 %U 10.19168/jyasar.687093
ISNAD Dobrucalı, Birce , İlter, Burcu . "Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis". Yaşar Üniversitesi E-Dergisi 16 / 61 (Ocak 2021): 95-127 . https://doi.org/10.19168/jyasar.687093
AMA Dobrucalı B. , İlter B. Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis. Yaşar Üniversitesi E-Dergisi. 2021; 16(61): 95-127.
Vancouver Dobrucalı B. , İlter B. Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis. Yaşar Üniversitesi E-Dergisi. 2021; 16(61): 95-127.
IEEE B. Dobrucalı ve B. İlter , "Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis", Yaşar Üniversitesi E-Dergisi, c. 16, sayı. 61, ss. 95-127, Oca. 2021, doi:10.19168/jyasar.687093