# Emotion Detection and Analysis on Social Media

arXiv: https://arxiv.org/abs/1901.08458 · HTML: https://mlsanity.com/paper/1901.08458/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 15 of 15)

- [HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition](https://mlsanity.com/paper/2304.06910/) (2023)
- [MMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMs](https://mlsanity.com/paper/2505.24423/) (2025)
- [EmoMix-3L: A Code-Mixed Dataset for Bangla-English-Hindi Emotion Detection](https://mlsanity.com/paper/2405.06922/) (2024)
- [Impact of Stickers on Multimodal Sentiment and Intent in Social Media: A New Task, Dataset and Baseline](https://mlsanity.com/paper/2405.08427/) (2024)
- [Detect Depression from Social Networks with Sentiment Knowledge Sharing](https://mlsanity.com/paper/2306.14903/) (2023)
- [Balancing Task-invariant Interaction and Task-specific Adaptation for Unified Image Fusion](https://mlsanity.com/paper/2504.05164/) (2025)
- [An Annotated Corpus of Arabic Tweets for Hate Speech Analysis](https://mlsanity.com/paper/2505.11969/) (2025)
- [Identifying Features Associated with Bias Against 93 Stigmatized Groups in Language Models and Guardrail Model Safety Mitigation](https://mlsanity.com/paper/2512.19238/) (2025)
- [A Mixture-of-Experts Model for Multimodal Emotion Recognition in Conversations](https://mlsanity.com/paper/2602.23300/) (2026)
- [Age Range Estimation using MTCNN and VGG-Face Model](https://mlsanity.com/paper/2104.08585/) (2021)
- [Analysing the Greek Parliament Records with Emotion Classification](https://mlsanity.com/paper/2205.12012/) (2022)
- [Emotion Detection From Social Media Posts](https://mlsanity.com/paper/2302.05610/) (2023)
- [ECRC: Emotion-Causality Recognition in Korean Conversation for GCN](https://mlsanity.com/paper/2403.10764/) (2024)
- [Evaluating LLMs Capabilities Towards Understanding Social Dynamics](https://mlsanity.com/paper/2411.13008/) (2024)
- [Emotion Alignment: Discovering the Gap Between Social Media and Real-World Sentiments in Persian Tweets and Images](https://mlsanity.com/paper/2504.10662/) (2025)

## Abstract (arXiv)

In this paper, we address the problem of detection, classification and quantification of emotions of text in any form. We consider English text collected from social media like Twitter, which can provide information having utility in a variety of ways, especially opinion mining. Social media like Twitter and Facebook is full of emotions, feelings and opinions of people all over the world. However, analyzing and classifying text on the basis of emotions is a big challenge and can be considered as an advanced form of Sentiment Analysis. This paper proposes a method to classify text into six different Emotion-Categories: Happiness, Sadness, Fear, Anger, Surprise and Disgust. In our model, we use two different approaches and combine them to effectively extract these emotions from text. The first approach is based on Natural Language Processing, and uses several textual features like emoticons, degree words and negations, Parts Of Speech and other grammatical analysis. The second approach is based on Machine Learning classification algorithms. We have also successfully devised a method to automate the creation of the training-set itself, so as to eliminate the need of manual annotation of large datasets. Moreover, we have managed to create a large bag of emotional words, along with their emotion-intensities. On testing, it is shown that our model provides significant accuracy in classifying tweets taken from Twitter.