[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.