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A review on sentiment analysis and emotion detection from text.
Pansy Nandwani1, Rupali Verma1
1Computer Science and Engineering Department, Punjab Engineering College, Chandigarh, India.
Social Network Analysis and Mining
|September 6, 2021
Summary
This review explores sentiment analysis and emotion detection from text on social media. It details methods, models, and challenges in understanding user emotions online.
Area of Science:
- Natural Language Processing
- Computational Linguistics
- Affective Computing
Background:
- Social media generates vast amounts of unstructured text data daily.
- Understanding public sentiment and emotions is crucial for various applications.
- Text-based communication on social platforms requires advanced analytical tools.
Purpose of the Study:
- To review sentiment analysis and emotion detection techniques for textual data.
- To provide insights into different emotion models and their applications.
- To discuss the methodologies involved in analyzing sentiment and emotions from text.
Main Methods:
- Review of existing literature on sentiment analysis and emotion detection.
- Analysis of various emotion models and their theoretical underpinnings.
- Examination of the processes for extracting sentiment and emotion from text.
Main Results:
- Sentiment analysis identifies text polarity (positive, negative, neutral).
- Emotion detection offers a more granular understanding of user mental states.
- Both techniques are vital for comprehending human psychology in digital communication.
Conclusions:
- Sentiment analysis and emotion detection are essential for processing social media data.
- Challenges exist in accurately analyzing sentiment and emotions from text.
- Further research is needed to refine these analytical methods.
