• G.M. BALAJI
• K. VADIVAZHAGAN

This research article delves into the utilization of various machine learning techniques for determining the emotion or attitude conveyed in the text, known as sentiment analysis. Vector support machines, choice trees, and naive Bayes are just a few examples of supervised approaches, and unstructured methods like clustering and topic modelling are being explored. Additionally, it highlights the challenges and limitations of using machine learning in sentiment analysis, including the requirement for substantial amounts of labeled data and the complexity of comprehending sarcasm and irony. The study's outcomes indicate that machine learning can be a useful tool for sentiment analysis and that a combination of different techniques may be required to achieve optimal results.

Sentiment, Classification, Amazon, Machine Learning, Prediction

"SENTIMENT ANALYSIS USING MACHINE LEARNING: A SHORT REVIEW ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.f166-f173, December-2023, Available :http://www.jetir.org/papers/JETIR2312519.pdf

"SENTIMENT ANALYSIS USING MACHINE LEARNING: A SHORT REVIEW ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppf166-f173, December-2023, Available at : http://www.jetir.org/papers/JETIR2312519.pdf