New Frontiers in Communication and Intelligent Systems
Textual Sentiment Analysis using Machine Learning and NLP: A Review
Authors: Neha Sharma, S Veenadhari and Rachna Kulhare
Publishing Date: 06-05-2022
ISBN: 978-81-95502-00-4
Abstract
One of the most prominent parts of opinion mining is sentiment analysis that is used to extract emotion from textual features. Through this process, it can be determined whether a piece of writing is positive, negative, or neutral. This paper presents a review on the sentiment analysis task using textual data. A sentiment analysis system for text com-bines Natural Language Processing (NLP) and Machine Learning Techniques to assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase. One of the prominent parts of opinion mining is depression detection. Nowadays, a significant section of society is affected by depression due to mental stress. So, it is also a considerable concern. There may be several reasons for depression, especially in adults. Different people have different symptoms, and its identification is a significant challenge. Most people feel shy to accept that they suffer from depression, while some people are unaware of their depressed mental health. The objective of this paper is to analyze recently developed frameworks for the diagnosis of sentiment and depression level of an individual. This paper directs towards creating a single framework in the future for depression and sentiment analysis.
Keywords
Sentiment Analysis, Natural Language Processing, Depression Detection, Emotion recognition, Machine Learning.
Cite as
Neha Sharma, S Veenadhari and Rachna Kulhare, "Textual Sentiment Analysis using Machine Learning and NLP: A Review", In: Rahul Srivastava and Aditya Kr. Singh Pundir (eds), New Frontiers in Communication and Intelligent Systems, SCRS, India, 2022, pp. 497-505. https://doi.org/10.52458/978-81-95502-00-4-51