Sentiments Unleashed : Pioneering the Frontier of Sentiment Analysis through Cutting-Edge Applications and Methodologies

Ravi Lourdusamy; Prabakaran Thangavel; Johnbosco S

doi:10.32628/CSEIT24105105

Authors

Ravi Lourdusamy Sacred Heart College (Autonomous), Tirupattur(DT), Thiruvalluvar University, Tamilnadu, India Author
Prabakaran Thangavel Sacred Heart College (Autonomous), Tirupattur(DT), Thiruvalluvar University, Tamilnadu, India Author
Johnbosco S Sacred Heart College (Autonomous), Tirupattur(DT), Thiruvalluvar University, Tamilnadu, India Author

DOI:

https://doi.org/10.32628/CSEIT24105105

Keywords:

Emotions, Attitudes, Opinions, Lexicon-Based, Machine Learning

Abstract

Sentiment analysis is a powerful tool within Natural Language Processing (NLP), assists businesses and researchers in discerning the emotions, attitudes, and beliefs conveyed in written language. The increasing availability of textual data, including social media posts, product reviews, and customer feedback, has driven a significant increase in the demand for sentiment analysis. The primary goal is to uncover underlying emotions expressed through diverse word combinations, sentences, or paragraphs. By scrutinizing language nuances, it identifies a wide range of emotions, from joy and sadness to criticism and admiration. This profound understanding, facilitated by sentiment analysis, provides valuable insights into public perception, market trends, and consumer behaviours, empowering businesses to refine strategies, improve products, and enhance customer experiences. As sentiment analysis tools and techniques continue to advance, they present opportunities for progress, assisting businesses in navigating shifts in public opinion and enabling researchers to delve into the intricacies of human expression through text. With the continuous surge in textual data, sentiment analysis retains its significance in guiding stakeholders from diverse industries toward informed decisions and a comprehensive understanding of human sentiment. This research explores advanced approaches in sentiment analysis, including machine learning, lexicon-based methods, hybrid and ontology-based models, and multi-modal techniques, examining various levels, classifications, and methodologies.

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References

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