2nd International Conference on Materials Science and Sustainable Manufacturing Technology
Multi-Trend Twitter Sentiment Analysis: Collaborative Approach for Improved Results
Keerthika. J, Hemapriya. N, Abinaya. R, Karpagathareni. S, Bhavadharani. S, Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, India.
Twitter has a significant number of daily users through which tweets are utilized to communicate their thoughts
in this era of growing social media users. This paper offers a way to haul out sentiments from tweets as well as a method
for sorting out various tweets as optimistic, adverse, or unbiased. It refers to identifying and classifying the sentiments
expressed in the text source. The existing Twitter APIs for data extraction are used to mine public Twitter data. Tweets
would be chosen based on a few carefully chosen keywords related to the domain of our concern. In our proposed method,
we collected various sentiment data from a variety of tweets to train and produce more precise and reliable sentiment
classifiers for each trend. This method automatically extracts the key elements of subjects from online user evaluations.
Since tweets are generally unstructured in format, they must first be converted into structured format. And after that, the
data is fed into several models for training and used to rank the best sentiment classifier. The intention of this design is to
arrive at a model that can classify sentiments of real-world data using Twitter.
Keywords
Recommendation System, Sentiment Analysis, Literature Review, Content-Based Filtering.
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Cite this article
Keerthika. J, Hemapriya. N, Abinaya. R, Karpagathareni. S, Bhavadharani. S, “Multi-Trend Twitter Sentiment Analysis: Collaborative Approach for Improved Results”, Advances in Computational Intelligence in Materials Science, pp. 066-072, May. 2023. doi:10.53759/acims/978-9914-9946-9-8_12
