SENTIMENT ANALYSIS IN MULTIPARTY CONVERSATION

Abstract

Sentiment analysis is a method for examining data in a particular kind of text and identifying its opinion-based content. It is also known as emotion mining or emotion extraction. These days, online communication tools like Twitter, Facebook, YouTube, and others are bringing a lot of zeal into people’s lives. People discuss it and express their opinions or feelings. In this review article, We decide to focus on opinion mining, also known as emotion evaluation, which is a subset of machine learning and online data mining. This article presents the findings of a study that combined multiple Machine Learning(ML) and Lexicon exploration techniques. To conduct an assessment study and validate the estimated quantity of the existing composition, the data are examined. Fu ture researchers will be better equipped to understand the existing beginnings of the arrangement of possibility evaluation as a consequence. Emotion recognition in conversations (ERC) is a field of study and technology that focuses on identifying and understanding emotions expressed in conversations. The goal is to automatically detect and categorize emotions in text-based conversations, such as emails, chats, and social media posts. ERC is interdisciplinary, drawing on fields such as Natural Language Processing (NLP), psychology, and computer science. It has appli cations in areas such as customer service, mental health, and market research. There is currently no database of emotional conversations with more than two speakers per dis cussion in regional Indian languages including Tamil, Telugu, Malayalam, and Bengali. I suggest the Dataset comprise roughly 2000+ utterances from 800+ conversations from the Indian film series Bahubali in order to fill this gap. Each speech will have textual Modalities and be tagged with emotion, personality, and sentiment labels.