International Journal For Multidisciplinary Research

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A Perspective Study on Tweet Sentiment Analysis using Data Mining, Machine Learning and Deep Learning Paradigms

Author(s) G.PRIYADARSHINI, Dr.D.KARTHIKA
Country India
Abstract In recent years there has been an increase in interest in collecting and studying text from social networks, review websites, blogs, forums and other forms of user-generated information. The text offers a vast array of ideas from people of diverse profiles, including education, age and their perspectives, region of residence, on how they see goods and services, policy opinions, etc. The analysis of judgments, responses, and emotions drawn from texts is known as sentiment analysis. The sentiment categorization procedure establishes whether a text is subjective or objective, or whether it provokes both positive and negative responses. The most popular method of classification is based on polarity or orientation for accomplishing tweet sentiment analysis. In this paper, a detailed survey on various algorithms used for performing opinion mining, sentiment analysis, tweet sentiment analysis is discussed in detail. The study shows that text preprocessing, data mining, machine learning algorithm and deep learning paradigms plays a vital role in categorization of people’s feeling on a specific topic or a product. In this study, the existing challenges in optimizing the process of tweet sentiment analysis is also discussed and the suggestions for improving is also discussed.
Keywords opinion mining, tweets, sentiment analysis, machine learning, deep learning, polarization
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-12-31
Cite This A Perspective Study on Tweet Sentiment Analysis using Data Mining, Machine Learning and Deep Learning Paradigms - G.PRIYADARSHINI, Dr.D.KARTHIKA - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.10861
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.10861
Short DOI https://doi.org/gtbtj9
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