Twitter Sentiment Analysis System

TL;DR

This paper discusses methods for sentiment analysis of textual data from social media, highlighting its applications in mental health and community well-being, and reviews techniques from NLP and machine learning.

Contribution

It provides an overview of sentiment recognition techniques based on textual data and discusses their relevance and applications.

Findings

Sentiment analysis can identify mental health issues from social media texts.

Various NLP and machine learning techniques are used for sentiment classification.

Sentiment analysis has broad applications in monitoring community well-being.

Abstract

Social media is increasingly used by humans to express their feelings and opinions in the form of short text messages. Detecting sentiments in the text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or mood of a community. Sentiments can be expressed in many ways that can be seen such as facial expression and gestures, speech and by written text. Sentiment Analysis in text documents is essentially a content-based classification problem involving concepts from the domains of Natural Language Processing as well as Machine Learning. In this paper, sentiment recognition based on textual data and the techniques used in sentiment analysis are discussed.