Abstract
In this current era, sentiment analysis and also recommendation systems are some of the most popular subjects for the majority of the researchers, and on the other hand, these are the most important things for our regular life. The aim is to make valid recommendations and the most relevant tweets to the user of this model by extracting and analyzing random tweets based on a particular user address or hashtag. Nowadays, people value time over money, so we aim to save the user his or her most precious time and recommend the pertinent tweets. This model will filter out all the irrelevant as well as the inappropriate tweets and try to provide the gravest and valued tweets from Twitter. This filtration will happen based on the tweet’s public features as well as the attributes of the respective tweeter such as his or her follower count. The most initial concepts of machine learning, recommendation system, and sentiment analysis are understood and implemented to be able to propose the mentioned model. The tools to work with the mentioned topics are implemented to evaluate prominent output with utmost effort throughout. Let alone being used in product recommendation systems, our proposed method is also capable enough to maintain a consistent and effective performance in other fields.
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Dutta, S., Mondal, S., Sarkar, D. (2022). Design and Implementation of Recommendation System Using Sentiment Analysis in Social Media. In: Das, K.N., Das, D., Ray, A.K., Suganthan, P.N. (eds) Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6893-7_14
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