• Eshwari Kulkarni
• Dr. Anita M. Pujar

Abstract—Sentimental research, also known as "Opinion mining," is concerned with evaluating attitudes and classify opinions based on reviews or comments. SA has emerged as a leading research area of Natural Language Processing (NLP). The role of Sentiment Analysis (SA) is to categorize people's views as positive or negative based on a specific statement or evaluation. Review and opinions play a significant part in determining the degree of happiness of consumers with a specific organization. Since human perceptions help to improve product quality, and the popularity or loss of a movie is determined by its ratings, there is an increased demand and need for building a good sentiment analysis model that classifies movie reviews. The research article offers a study of emerging methods for categorizing sentimental analysis in general, as well as a summary of the key research problems posed in recent domain like sarcasm detection. We discovered that machine learning-based methods, such as supervised learning, unsupervised learning techniques, and Lexicon-based techniques, are the most commonly utilized. Research on different data sources has been supported using various algorithms such as Naive Bayes, SVM, and Ensemble classifier.

Keywords: Sentiment analysis, Natural Language Processing, machine learning approaches; Lexicon-based approaches, sarcasm detection, Sarcasm Detection.

"Intelligent Framework for Sentiment Analysis of Movie Reviews - A Comprehensive Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.f738-f743, June-2021, Available :http://www.jetir.org/papers/JETIR2106802.pdf

"Intelligent Framework for Sentiment Analysis of Movie Reviews - A Comprehensive Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppf738-f743, June-2021, Available at : http://www.jetir.org/papers/JETIR2106802.pdf