ارائه الگوی تحلیل بازخورد متنی کاربران در شبکه‌های اجتماعی در مواجهه با رویدادها و کنش‌های محیطی

نویسندگان
احمد مجلس آرا 1
فاطمه فهیم نیا 2
نادر نقشینه 3
1 پردیس بین المللی کیش دانشگاه تهران
2 دانشگاه تهران، دانشکده مدیریت، تهران، ایران
3 دانشگاه تهران
چکیده
زمینه و هدف: پژوهش حاضر با هدف تدوین و اعتباریابی الگوی تحلیل بازخورد متنی کاربران در شبکه­‌های اجتماعی در مواجهه با رویدادها و کنش­‌های محیطی با تأکید بر شناسایی عوامل مؤثر بر ارائه­‌ی پیام­های متنی توسط کاربران در شبکه­های اجتماعی انجام شده است.

روش پژوهش: از لحاظ رویکرد پژوهش، روش امیخته اکتشافی بکار برده شده است. در مرحله‌­ی اول این پژوهش از روش فراترکیب استفاده شد که از جمله روش‌­های کیفی به شمار می­‌رود. در مرحله­‌ی دوم برای بررسی و اعتباریابی عوامل شناسایی شده و تهیه الگوی نهایی تحقیق، از روش پیمایشی یعنی نظرسنجی از خبرگان در قالب تشکیل گروه‌­های کانوی و استفاده از پرسشنامه استفاده شده است. جامعه آماری اول پژوهش کلیه اسناد مکتوب مربوط به تحلیل بازخورد متنی و احساسات کاربران شامل 60 مقاله و اثر بر مبنای معیارهای معتبر از بین 198 مقاله و اثر انتخاب و تحلیل شد. جامعه‌­ی آماری دوم شامل خبرگان حوزه­‌ی فناوری اطلاعات در رشته­‌های فناوری اطلاعات، جامعه­‌شناسی، رفتارشناسی و... بودند که در نهایت 15 نفر تعیین شدند و به دلیل شرایط و محدودیت­های پاندمی کرونا، به صورت از راه دور نسبت به ارائه‌­ی نظرات و پیشنهادات اقدام نمودند.

یافته­‌ها: با کاربرد مراحل هفتگانه فراترکیب، الگوی مفهومی در شش لایه­‌ی مفهومی، مقوله­‌ای و کدها به دست امد. در هر یک از لایه‌­ها مفاهیم و موضوعاتی گنجانده شد که در نهایت 27 مؤلفه شناسایی شد. جهت اعتباریابی کیفی الگوی به دست آمده بر اساس نظرات خبرگان در قالب گروه­‌های کانونی اخذ شد و الگوی مفهومی مورد تأیید خبرگان پژوهش قرار گرفت.

نتیجه‌­گیری: با توجه به یافته­‌های حاصل از اجرای فرآیند فراترکیب در شناسایی ابعاد و مقوله‌­های رفتاری کاربران در تحلیل بازخورد متنی در شبکه­‌های اجتماعی که می‌­تواند مبنایی برای پژوهش­‌های آتی در جهت هدایت، راهبری و رفتارشناسی کاربران و همچنین راهنمایی در راستای سیاست­‌های اجرایی برای مسئولین و تصمیم‌­گیران در سازمان­‌ها و نهادهای ذی‌­نقش قرار گیرد در قالب الگویی مفهومی که به تأیید و توافق بالای خبرگان رسید، ارائه گردید.
کلیدواژه‌ها
الگو
تحلیل بازخورد کاربران
شبکه‌های اجتماعی
کنش‌های محیطی

عنوان مقاله English

Proposing a Model for Analyzing Textual Feedback from Users on Social Networks Facing Environmental Events and Actions

نویسندگان English

Ahmad Majlesara 1
Fatemeh Fahim niya 2
Nader Naghshineh 3
1 University of Tehran Kish International Campus
2 University of Tehran
3 University of Tehran
چکیده English

Background and Objective: The study aims to develop and validate a model for analyzing the textual feedback of users in social networks in the face of environmental events and actions with emphasis on identifying the factors affecting the presentation of text messages by users in social networks.

Research Methodology: Heuristic mixed method has been used. In the first stage, the meta-combined method was applied with a qualitative basis. In the second stage, to inspect, validate the identified factors and prepare the final research model, the survey method via questionnaire and forming conveyor group was combined. Population consisted of: 1) Selection and analysis of written documents related to the analysis of textual feedback and users' feelings, including 60 articles and works based on valid criteria from among 198 articles and works; 2) Experts in information technology, sociology, behaviorism, etc., which 15 people were selected, but as a result and limitations of the corona pandemic comments and suggestions were remotely submitted.

Results: Using the seven steps of meta-combination, a conceptual pattern was obtained in six conceptual layers, categories and codes. In each layer, concepts and topics were included, and to end 27 components were identified. For qualitative validation, the obtained model was found based on the opinions of experts in the form of focus groups and the conceptual model was approved by the research experts.

Conclusion: The conceptual model - obtained from the hybrid stages and focus groups – which has been approved and accepted by experts could be used as a basis for future research to guide, and direct the behavior of users in social networking in order to provide strategies and executive policies for officials and decision makers in relevant organizations and institutions.

کلیدواژه‌ها English

Model
Feedback Analysis
Users
Social networks
Environmental Actions
Alvarez-Milan, Agarzelim, Reto Felix, Philipp A. Rauschnabel, and Christian Hinsch. 2018. "Strategic User engagement: A decision making framework." Journal of Business Research 61-70. doi: [DOI:10.1016/j.jbusres.2018.07.017.]
Alvarez-Milan, Agarzelim, Reto Felix, Philipp A. Rauschnabel, and Christian Hinsch. 2018. "Strategic User engagement: A decision making framework." Journal of Business Research 61-70. doi: [DOI:10.1016/j.jbusres.2018.07.017]
Basiriyan , H. Khaniky, H. (2014). Iranian policymakers and social media policymakers. So-cial Welfare and Development Planning Quarterly, No. 21. (Persian)
Basiriyan , H. Khaniky, H. (2014). Iranian policymakers and social media policymakers. So-cial Welfare and Development Planning Quarterly, No. 21. (Persian)
Basiri.E. Kabiri,A. (2017). Translation is not enough: Comparing Lexicon-based methods for sentiment analysis in Persian.Computer Science and Software Engineering. [DOI:10.1109/CSICSSE.2017.8320114]
Basiri.E. Kabiri,A. (2017). Translation is not enough: Comparing Lexicon-based methods for sentiment analysis in Persian.Computer Science and Software Engineering.
Barbosa.L., Feng.J., (2010). Robust Sentiment Detection on Twitter from Biased and Noisy Data. Journal of Information Technology.
Barnaghi, P., Ghaffari, P., & Breslin, J. G. (2016). Opinion mining and sentiment polarity on twitter and correlation between events and sentiment. In 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService) (pp. 52-57). IEEE. [DOI:10.1109/BigDataService.2016.36]
Barbosa.L., Feng.J., (2010). Robust Sentiment Detection on Twitter from Biased and Noisy Data. Journal of Information Technology.
Basiri, M. E., & Kabiri, A. (2017). Sentence-level sentiment analysis in Persian. 3rd International Conference on Pattern Analysis and Image Analysis, IPRIA 2017, (April), 84-89. [DOI:10.1109/PRIA.2017.7983023]
Barnaghi, P., Ghaffari, P., & Breslin, J. G. (2016). Opinion mining and sentiment polarity on twitter and correlation between events and sentiment. In 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService) (pp. 52-57). IEEE.
Bhattacharya, C.B., H. Rao, and M.A. Glynn. 1995. "Understanding the bond of identification: an investigation of its correlates among art museum members." Journal of Marketing 59 (4): 46-57. [DOI:10.1177/002224299505900404]
Bryman, A., and E. Bell. 2007. Business research methods. Oxford University Press.
Basiri, M. E., & Kabiri, A. (2017). Sentence-level sentiment analysis in Persian. 3rd International Conference on Pattern Analysis and Image Analysis, IPRIA 2017, (April), 84-89. [DOI:10.1109/PRIA.2017.7983023.]
Carlson, Jamie, Jessica Wyllie, Mohammad M. Rahman, and Ranjit Voola. 2018. "Enhancing brand relationship performance through customer participation." Journal of Retailing and Consumer Services 1-9. doi:
Consuegra, D. and Klieb, L. (2018). Impact of Social Media on Consumer Behaviour. International Journal of Information and Decision Sciences, 11(3). [DOI:10.1504/IJIDS.2019.101994]
Bhattacharya, C.B., H. Rao, and M.A. Glynn. 1995. "Understanding the bond of identification: an investigation of its correlates among art museum members." Journal of Marketing 59 (4): 46-57.
Drus, Z. Khalid,H. (2019). Sentiment Analysis in Social Media and Its Application: Systematic Literature Review, Procedia Computer Science. [DOI:10.1016/j.procs.2019.11.174]
Duwairi, R., & El-Orfali, M. (2014). A study of the effects of preprocessing strategies on sentiment analysis for Arabic text. Journal of Information Science, 40(4), 501-513. [DOI:10.1177/0165551514534143]
Bryman, A., and E. Bell. 2007. Business research methods. Oxford University Press.
Feely, W., Manshadi, M., Frederking, R. E., & Levin, L. S. (2014). The CMU METAL Farsi NLP Approach. In LREC (pp. 4052-4055).
Carlson, Jamie, Jessica Wyllie, Mohammad M. Rahman, and Ranjit Voola. 2018. "Enhancing brand relationship performance through customer participation." Journal of Retailing and Consumer Services 1-9. doi: [DOI:10.1016/j.jretconser.2018.07.008.]
Flick, U. 2002. An Introduction to Qualitative Research. London: Sage Publication.
Gilmore, J.H., and B.J. II Pine. 2002. "Differentiating hospitality operations via experiences: why selling services is not enough." The Cornell Hotel and Restaurant Administration Quarterly 43 (3): 87-96. [DOI:10.1016/S0010-8804(02)80022-2]
Consuegra, D. and Klieb, L. (2018). Impact of Social Media on Consumer Behaviour. International Journal of Information and Decision Sciences, 11(3).
Hajli, Nick, Mohana Shanmugam, Savvas Papagiannidis, Debra Zahay, and Marie-Odile Richard. 2017. "Branding co-creation with members of online brand communities." Journal of Business Research 70: 136-144. doi:http://dx.doi.org/10.1016/j.jbusres.2016.08.026. [DOI:10.1016/j.jbusres.2016.08.026]
Harmeling.M, Collen, Jordan W.Moffett, Mark J.Arnold, and Brad D.Carlson. 2016. "Toward a theory of user engagement marketing." journal of Academy of Marketing Science 45: 312-335. doi:10.1007/s11747-016-0509-2. [DOI:10.1007/s11747-016-0509-2]
Drus, Z. Khalid,H. (2019). Sentiment Analysis in Social Media and Its Application: Systematic Literature Review, Procedia Computer Science.
Hashim,K.F, Fadhil, N., Engaging with Customer Using Social Media Platform: A Case Study of Malaysia Hotels, Procedia Computer Science.
Hollebeek, Linda, Biljana Juric, and Wenyan Tang. 2017. "Virtual brand community engagement practices: a refined typology and model." Journal of Services Marketing 31 (3): pp.204-217. doi: [DOI:10.1108/JSM-01-2016-0006]
Duwairi, R., & El-Orfali, M. (2014). A study of the effects of preprocessing strategies on sentiment analysis for Arabic text. Journal of Information Science, 40(4), 501-513.
Hoseini,P. Ramaki, A., Maleki,H. (2021). SentiPers: A Sentiment Analysis Corpus for Persian, Journal of Information Management.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167 [DOI:10.1007/978-3-031-02145-9]
Feely, W., Manshadi, M., Frederking, R. E., & Levin, L. S. (2014). The CMU METAL Farsi NLP Approach. In LREC (pp. 4052-4055).
Muneta,M., Pascual, C., Lopez,A. 2020, Key Image Attributes to Elicit Likes and Comments on Instagram. Journal of Promotion Management.
Flick, U. 2002. An Introduction to Qualitative Research. London: Sage Publication.
Pansari, Anitta, and V.Kumar. 2016. "Customer engagement: the construct, antecedents, and consequences." Journal of Academy of Marketing Science 45 (3): 294-311. doi: [DOI:10.1007/s11747-016-0485-6]
Gilmore, J.H., and B.J. II Pine. 2002. "Differentiating hospitality operations via experiences: why selling services is not enough." The Cornell Hotel and Restaurant Administration Quarterly 43 (3): 87-96.
Peykary, N. Yaghobi, A. Taheri, H .(2015). Analyzing emotions on Twitter with text mining techniques. International Conference on Web Research. (Persian)
Pilan jezhad, M. (2018). Sentiment analysis of social networks users based on text mining. National Conference on New Technologies in Electrical and Computer Engineering. (Persian)
Hajli, Nick, Mohana Shanmugam, Savvas Papagiannidis, Debra Zahay, and Marie-Odile Richard. 2017. "Branding co-creation with members of online brand communities." Journal of Business Research 70: 136-144. doi:http://dx.doi.org/10.1016/j.jbusres.2016.08.026.
Potdar, Vidyasagar, Sujata Joshi, Rahul Harish, Richard Baskerville, and Pornpit Wongthongtham. 2018. "A process model for identifying online customer engagement patterns on." Information Technology & People 31 (2): 595-614. doi: [DOI:10.1108/ITP-02-2017-0035]
Rezaei, S. Dastkhan, H. Oliya, M. (2018). Text mining methods in analyzing customers' opinions and preferences in social networks: A case study in the Iranian digital products market. (Persian)
Harmeling.M, Collen, Jordan W.Moffett, Mark J.Arnold, and Brad D.Carlson. 2016. "Toward a theory of user engagement marketing." journal of Academy of Marketing Science 45: 312-335. doi:10.1007/s11747-016-0509-2.
Rietveld. R., Willemijn,V., (2020). What You Feel, Is What You Like Influence of Message Appeals on Customer Engagement on Instagram. [DOI:10.1016/j.intmar.2019.06.003]
Yue,L., Chen, W., Li,X., Yin,M. A survey of sentiment analysis in social media. Knowledge and Information Systems.
Hashim,K.F, Fadhil, N., Engaging with Customer Using Social Media Platform: A Case Study of Malaysia Hotels, Procedia Computer Science.
Xhema. J. Effect of Social Networks on Consumer Behavior: Complex Buying. (2019). IFAC PapaersOnline 52. [DOI:10.1016/j.ifacol.2019.12.594]
Hollebeek, Linda, Biljana Juric, and Wenyan Tang. 2017. "Virtual brand community engagement practices: a refined typology and model." Journal of Services Marketing 31 (3): pp.204-217. doi: [DOI:10.1108/JSM-01-2016-0006.]
Zakeri, R. Zakei, M. Fomani, GH. (2017). The relationship between the use of virtual social networks and the perception of social interactions. Information Technology Quarterly. (Persian)
Hoseini,P. Ramaki, A., Maleki,H. (2021). SentiPers: A Sentiment Analysis Corpus for Persian, Journal of Information Management.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167
Muneta,M., Pascual, C., Lopez,A. 2020, Key Image Attributes to Elicit Likes and Comments on Instagram. Journal of Promotion Management.
Pansari, Anitta, and V.Kumar. 2016. "Customer engagement: the construct, antecedents, and consequences." Journal of Academy of Marketing Science 45 (3): 294-311. doi: [DOI:10.1007/s11747-016-0485-6.]
Peykary, N. Yaghobi, A. Taheri, H .(2015). Analyzing emotions on Twitter with text mining techniques. International Conference on Web Research. (Persian)
Pilan jezhad, M. (2018). Sentiment analysis of social networks users based on text mining. National Conference on New Technologies in Electrical and Computer Engineering. (Persian)
Potdar, Vidyasagar, Sujata Joshi, Rahul Harish, Richard Baskerville, and Pornpit Wongthongtham. 2018. "A process model for identifying online customer engagement patterns on." Information Technology & People 31 (2): 595-614. doi: [DOI:10.1108/.]
Rezaei, S. Dastkhan, H. Oliya, M. (2018). Text mining methods in analyzing customers' opinions and preferences in social networks: A case study in the Iranian digital products market. (Persian)
Rietveld. R., Willemijn,V., (2020). What You Feel, Is What You Like Influence of Message Appeals on Customer Engagement on Instagram.
Yue,L., Chen, W., Li,X., Yin,M. A survey of sentiment analysis in social media. Knowledge and Information Systems.
Xhema. J. Effect of Social Networks on Consumer Behavior: Complex Buying. (2019). IFAC PapaersOnline 52.
Zakeri, R. Zakei, M. Fomani, GH. (2017). The relationship between the use of virtual social networks and the perception of social interactions. Information Technology Quarterly. (Persian)
تعامل انسان و اطلاعات
دوره 8، شماره 2
تابستان 1400
صفحه 12-24