Review on Sentimental Analysis and Aspect Analysis with Codemix using LLM, BERT, and Naive Bayes

Review on Sentimental Analysis and Aspect Analysis with Codemix using LLM, BERT, and Naive Bayes

Dr. Keerthi H M

Associate Professor,

B.E., M.Tech, Ph.D,

Dept. of Computer Science and Engg.

Malnad College of Engineering Hassan, India

Mr. Sudarshan K P

Dept of Computer Science and Engg.

Malnad College of Engineering Hassan, India
Mr. Siddarth S Mallik

Dept of Computer Science and Engg.

Malnad College of Engineering Hassan, India
Mr. Sumeeth S M

Dept of Computer Science and Engg.

Malnad College of Engineering Hassan, India

Mr. Sumit Chandra

Dept of Computer Science and Engg.

Malnad College of Engineering Hassan, India

Abstract
In the era of social media, where multilingual conversations are prevalent, analyzing code-mixed text poses unique challenges. This project presents a comparative analysis of sentiment analysis and aspect-based sentiment analysis on code-mixed data using advanced techniques like Large Language Models (LLM), BERT, and Naive Bayes.
Sentiment analysis categorizes text into positive, negative, or neutral sentiments, while aspect-based analysis identifies opinions on specific topics, such as "price" or "quality" in reviews. By focusing on code-mixed text, this study compares the effectiveness of each method in understanding sentiments and specific opinions, paving the way for improved applications in multilingual settings.