Abstract
In recent times, social media platforms have been acting as a significant means for framing a successful business. Its impact on the organizations is helping them to build an appropriate and confining atmosphere to help in the decision-making process which promotes customers to have a better experience in their regular practice. Convinced models have been developed using various social media platforms to refine the decision-making process, which indeed failed in providing persistent accuracy. In this research, we propose a model that minimizes the complications using sentiment analysis and performs certain steps of meticulous analysis which include influence and opinion analysis, data mining preprocessing techniques, machine learning and deep learning. To implement this model, we consider the input as the reviews and requirements of the customer who is utilizing the feature, to produce a valid and acceptable response. These results and evaluations will enable in enhancing the decision-making process of the organization.
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References
Halibas AS, Shaffi AS, Varusai Mohamed MAK (2018) Application of text classification and clustering of Twitter data for business analytics. In: 2018 Majan International Conference (MIC). IEEE (2018)
Sun S, Luo C, Chen J (2017) A review of natural language processing techniques for opinion mining systems. Inform Fusion 36:10–25
Rajput A (2020) Natural language processing, sentiment analysis, and clinical analytics. In: Innovation in health informatics. Academic, New York, pp 79–97
Bhattacharyya D, Biswas S, Kim T (2010) A review on natural language processing in opinion mining. Int J Smart Home 4(2):31–38
What is influence analysis—https://www.igi-global.com/dictionary/a-review-of-tools-for-overcoming-the-challenge-of-monitoring-of-social-media/34120
The 5 Clustering Algorithms Data Scientists Need to Know—https://towardsdatascience.com/the-5clustering-algorithms-data-scientists-need-to-know-a36d136ef68
Tan C, Lee L, Tang J, Jiang L, Zhou M, Li P (2011) User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1397–1405
Balahur A, Steinberger R, Kabadjov M, Zavarella V, Van Der Goot E, Halkia M, Pouliquen B, Belyaeva J (2013) Sentiment analysis in the news. arXiv preprint arXiv:1309.6202
Qiu G, Liu B, Bu J, Chen C (2009) Expanding domain sentiment lexicon through double propagation. In: Boutilier C (ed) IJCAI, pp 1199–1204
Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1:12
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Cherukuri, P.A.A., Linh, N.T.D., Indukuri, S., Nuthi, S. (2021). Sentiment Analysis Model and Its Role in Determining Social Media’s Influence on Decision Making. In: Kumar, R., Quang, N.H., Kumar Solanki, V., Cardona, M., Pattnaik, P.K. (eds) Research in Intelligent and Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-7527-3_34
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DOI: https://doi.org/10.1007/978-981-15-7527-3_34
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