• Hussain Hamid
• Shruti Bangare
• Amol Nirmal

Sentiment analysis is a rapidly evolving field in NLP that aims to understand and interpret human emotions, attitudes, and opinions expressed in text. This paper provides a comprehensive overview of sentiment analysis, covering its techniques, applications, challenges, and future directions. It discusses the lexicon-based approach, machine learning techniques, and deep learning models for sentiment analysis, and highlights its applications in customer feedback analysis, brand monitoring, social media sentiment tracking, and other domains. The paper also acknowledges the challenges and limitations of sentiment analysis, such as the risk of bias and privacy concerns, as well as the difficulties in handling sarcasm and irony. Finally, it discusses emerging trends in sentiment analysis, such as cross-lingual sentiment analysis, multimodal sentiment analysis, and transfer learning.

Sentiment analysis; opinion mining; natural language processing; lexicon-based approach; machine learning; deep learning; customer feedback analysis; brand monitoring; social media sentiment tracking; market research; discourse analysis; context-specific sentiments.

"Sentiment Analysis: A Comprehensive Review of Techniques and Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.c775-c779, August-2023, Available :http://www.jetir.org/papers/JETIR2308292.pdf

"Sentiment Analysis: A Comprehensive Review of Techniques and Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. ppc775-c779, August-2023, Available at : http://www.jetir.org/papers/JETIR2308292.pdf