طراحی سیستم‌ توصیه‌گر محصولات شوینده بر مبنای تحلیل احساسات فازی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت فناوری، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

2 استادیار، گروه مدیریت صنعتی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

3 استاد، گروه مدیریت صنعتی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

چکیده

امروزه شبکه‌های اجتماعی به عنوان محلی برای ارائه نظرات کاربران نسبت به موضوعات مختلف از جمله محصولات مصرفی تبدیل شده‌است و این نظرات به منبعی ارزشمند برای تحلیل احساسات و استخراج انتظارات مشتریان از محصولات تبدیل شده‌است. این موضوع فرصت مناسبی را برای شرکت‌ها جهت بازطراحی محصولات خود بر اساس نظرات کاربران فراهم کرده‌است. در این پژوهش برای ارائه توصیه به واحدهای طراحی محصولات شوینده، از تحلیل احساسات مشتریان و مصرف‌کنندگان این محصولات در شبکه‌های اجتماعی استفاده کرده‌ایم. بیش از 4200 مورد از نظرات کاربران شبکه اجتماعی توئیتر بر اساس موضوع پژوهش در سال 2019 استخراج و در مرحله پیش‌پردازش متون پالایش و تگ‌گذاری شدند و پس از طی این مرحله با بکارگیری منطق فازی و مدلیابی‌موضوعی به تحلیل احساسات پرداخته شده است. مدلیابی موضوعی را به منظور یافتن ویژگی‌های مورد اشاره در نظرات برای داشتن رویکردی بهتر در واحدهای طراحی محصولات بکار بردیم و از منطق فازی برای استحصال میزان قطبیت نظرات به 5 دسته بسیار‌مثبت، مثبت، خنثی، منفی و بسیار منفی استفاده کرده‌ایم. نتایج بدست‌آمده از تحلیل احساسات در مدل پژوهش با استفاده از ماتریس آشفتگی مورد ارزیابی قرار گرفت و صحت 86.15% حاصل شد.
در این پژوهش از زبان و کتابخانه های پایتون برای جمع‌آوری، پالایش و تحلیل اطلاعات استفاده گردید.

کلیدواژه‌ها


عنوان مقاله [English]

Designing a Recommendation System of Detergent Products with Fuzzy Sentiment Analysis Approach

نویسندگان [English]

  • majid behravan 1
  • Mohammadreza Motadel 2
  • Abbas Tolui Eshlaghi 3
  • Reza Radfar 3
1 PhD Student, Department of Technology Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 Assistant Professor, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 Professor, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
چکیده [English]

Today, social media has become a place for users to comment on various topics, including consumer products, and these comments have become a valuable resource for sentiments analyzing and extracting customer expectations of products. This subject provides companies with a good opportunity to redesign their products based on user feedback. In this study, to provide recommendations to the design units of detergent products, we used sentiment analysis of customers and consumers of this products on social media. More than 4200 tweets were extracted from Twitter in 2019 based on the research topic and refined and tagged during the pre-processing of the texts. Afterwards, we analyzed the emotions using fuzzy logic and topic modeling. We have used topic modeling to find the features mentioned in the comments for a better approach in the design units, and fuzzy logic to obtain the degree of polarity of ideas into 5 categories: very positive, positive, neutral, negative and very negative. We used confusion matrix for evaluating research model and an accuracy of 86.15 % has been recorded.
In this research Python libraries are used for data gathering, cleansing and analysis.

کلیدواژه‌ها [English]

  • Sentiments Analysis
  • Recommendation System
  • Topic Modeling
  • Business intelligence
  • Fuzzy logic
[1]          Khan, F. H., Qamar, U., & Bashir, S. (2016). ‘SentiMI: Introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection’. Applied Soft Computing, 39, pp.140-153.
[2]          Lei Wang, Jianwei Niu, Shui Yu. (2019). ‘SentiDiff: Combining Textual Information and Sentiment Diffusion Patterns for Twitter Sentiment Analysis’, IEEE Transactions on Knowledge and Data Engineering .Volume: 32, Issue: 10.
[3]          Zohreh Dehdashti Shahrokh, Maryam Naeli. (2020) ‘The Impact of Social Media Marketing Activities On Customer Equity of Luxury Brands A Study of Dorsa Brand’. Journal of Management Research in Iran.
[4]          Gamon, M., Aue, A., Corston-Oliver, S., and Ringger, E.(2005). ‘Pulse: Mining customer opinions from free text’, In Advances in Intelligent Data Analysis VI , pp. 121-132. Springer Berlin Heidelberg.
[5]          Azim Zarei, Davood Feiz, Ghazale Taheri .(2020). ‘Providing Social Market Intelligence Framework based on web 2.0 Using Text-Mining Technique on Social Media Websites (Case Study: Competitive Analysis between Samsung and Emersun Brands)’. Journal of Management Research in Iran.
[6]          N Srivats Athindran, S. Manikandaraj, R. Kamaleshwar. (2018).’ Comparative Analysis of Customer Sentiments on Competing Brands using Hybrid Model Approach’, 3rd International Conference on Inventive Computation Technologies (ICICT).
[7]          Ali Mohaghar, Seyed Hojjat Bazazzadeh, Roya Eghbal. (2017) “Identification and Prioritization of Effective Factors on Online Advertising in Iran's Market by Use of Fuzzy MADM Technics (Case Study: Clothing Industry) ”; Journal of Modern Research in Decision Making.
[8]          Omid afsharizadeh jafari,Morteza Maleki MinBashRazgah, Azim Zarei, Mohsen Shafiei Nikabadi. (2021). “Designing a ranking system for purchased products based on the consumer’s and expert’s opinions using an aspect-based sentiment analysis approach”; Journal of Modern Research in Decision Making.
[9]          Lin, Y., Zhang, J., Wang, X., and Zhou, A. (2012). ‘An information theoretic approach to sentiment polarity classification’, In Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality, pp. 35-40. ACM.
[10]        Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press.
[11]        Brooke, J. (2009). ‘A semantic approach to automated text sentiment analysis’, Doctoral dissertation, Simon Fraser University.
[12]        Liu, B. (2012). ‘Sentiment analysis and opinion mining’, Synthesis lectures on human language technologies, 5(1), pp.1-167.
[13]        Pang, B. and Lee, L. (2008). ‘Opinion mining and sentiment analysis’, Foundations and trends in information retrieval, 2(1-2), pp.1-135.
[14]        Esuli, A, and Sebastiani, F. (2005). ‘Determining the semantic orientation of terms through gloss classification’, In Proceedings of the 14th ACM International Conference on Information and Knowledge Management. Bremen, Germany.
[15]        Medhat, W., Hassan, A. and Korashy, H. (2014). ‘Sentiment analysis algorithms and applications: A survey,’Ain Shams Engineering Journal, 5(4), pp.1093-1113.
[16]        Montejo-Ráez, A., Martínez-Cámara, E., Martin-Valdivia, M. T. and Urena-Lopez, L. A. (2012). ‘Random walk weighting over sentiWordNet for sentiment polarity detection on twitter’. In Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, pp. 3-10). Association for Computational Linguistics.
[17]        Saif, H., He, Y. and Alani, H. (2012). ‘Semantic sentiment analysis of twitter’, In The Semantic Web–ISWC 2012, pp. 508-524. Springer Berlin Heidelberg.
[18]        Ortega, R., Fonseca, A. and Montoyo, A. (2013). ‘SSA-UO: Unsupervised Twitter sentiment analysis’, In Second Joint Conference on Lexical and Computational Semantics (* SEM) Vol. 2, pp. 501-507.
[19]        Balage Filho, P. P. and Pardo, T. A. (2013). ‘Nilc usp: A hybrid system for sentiment analysis in twitter messages’.In Second Joint Conference on Lexical and Computational Semantics (* SEM),Vol. 2, pp. 568-572.
[20]        Jain, A. K., & Pandey, Y. (2013). ‘Analysis and Implementation of Sentiment Classification Using Lexical POS Markers’, International Journal, 2(1).
[21]        Dhande, L., and Patnaik, G. (2014). ‘Analyzing sentiment of movie review data using Naive Bayes neural classifier’, Int J Emerg Trends Technol Comput Sci, 3, pp.313320.
[22]        Khan, F. H., Qamar, U., & Bashir, S. (2016). ‘SentiMI: Introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection’. Applied Soft Computing, 39, pp.140-153.
[23]        Li Yang, Ying Li, Jin Wang, R. Simon Sherratt, (2020), ‘Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning’, IEEE Access ( Volume: 8).
[24]        Prabowo, R. and Thelwall, M. (2009). ‘Sentiment analysis: a combined approach’, Journal of Informetrics,3,pp.143–157.