Twitter Sentiment Analysis System
This is an incremental review of existing methods for sentiment analysis in social media, relevant for researchers and practitioners in NLP and mental health monitoring.
The paper addresses sentiment analysis in social media text, discussing techniques for detecting feelings and opinions, with applications in identifying individual anxiety or depression and measuring community well-being.
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.