A Comprehensive Review Of Machine Learning Approaches For Sentiment Analysis Of Code-Mixed Social Media Text

Anita Anita; Ajit Kumar

doi:10.56975/ijcrt.v14i3.304250

January 1, 2026INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTSOpen Access

A Comprehensive Review Of Machine Learning Approaches For Sentiment Analysis Of Code-Mixed Social Media Text

View Full Paper

Ask AI

Bookmark

Share

Key Points

The aim is to evaluate various machine learning techniques for analyzing sentiment in code-mixed social media texts.
Reviewed existing literature on sentiment analysis techniques using machine learning.
Compiled different experiments and their outcomes related to code-mixed text.
Analyzed the effectiveness of various algorithms in sentiment classification.
Identified key algorithms used for sentiment analysis in code-mixed texts.
Revealed strengths and weaknesses of different approaches in handling mixed languages.
Highlighted the need for more robust models to improve sentiment accuracy.

Implication

This comprehensive review analyzes machine learning methods for sentiment analysis of code-mixed social media text, suggesting implications for future research.

Abstract

No abstract yet. The journal hasn't published one we can show.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Anita et al. (2026) studied this question.

synapsesocial.com/papers/69d0af1c659487ece0fa5011 https://doi.org/10.56975/ijcrt.v14i3.304250

View Full Paper

Ask AI

Bookmark

Share

A Comprehensive Review Of Machine Learning Approaches For Sentiment Analysis Of Code-Mixed Social Media Text

Key Points

Implication

Abstract

Discussion

Cite This Study

A Comprehensive Review Of Machine Learning Approaches For Sentiment Analysis Of Code-Mixed Social Media Text

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading