# Sentiment Analysis of Twitter Data: A Survey of Techniques

arXiv: https://arxiv.org/abs/1601.06971 · HTML: https://mlsanity.com/paper/1601.06971/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2016.

## Built on by (most cited first, 16 of 16)

- [Unsupervised Layer-wise Score Aggregation for Textual OOD Detection](https://mlsanity.com/paper/2302.09852/) (2023)
- [TI-Capsule: Capsule Network for Stock Exchange Prediction](https://mlsanity.com/paper/2102.07718/) (2021)
- [Task-oriented Dialogue Systems: performance vs. quality-optima, a review](https://mlsanity.com/paper/2112.11176/) (2021)
- [Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020](https://mlsanity.com/paper/2303.12029/) (2023)
- [Detecting Inspiring Content on Social Media](https://mlsanity.com/paper/2109.02734/) (2021)
- [A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks](https://mlsanity.com/paper/2207.02160/) (2022)
- [Pruning General Large Language Models into Customized Expert Models](https://mlsanity.com/paper/2506.02561/) (2025)
- [SenWave: A Fine-Grained Multi-Language Sentiment Analysis Dataset Sourced from COVID-19 Tweets](https://mlsanity.com/paper/2510.08214/) (2025)
- [A Study on Herd Behavior Using Sentiment Analysis in Online Social Network](https://mlsanity.com/paper/2108.01728/) (2021)
- [A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot](https://mlsanity.com/paper/2201.02735/) (2021)
- [Sentiment Analysis of Political Tweets for Israel using Machine Learning](https://mlsanity.com/paper/2204.06515/) (2022)
- [Sentiment Analysis of Cybersecurity Content on Twitter and Reddit](https://mlsanity.com/paper/2204.12267/) (2022)
- [Public Reaction to Scientific Research via Twitter Sentiment Prediction](https://mlsanity.com/paper/2209.07333/) (2022)
- [Twitter Data Analysis: Izmir Earthquake Case](https://mlsanity.com/paper/2212.01453/) (2022)
- [Comparative Study of Sentiment Analysis for Multi-Sourced Social Media Platforms](https://mlsanity.com/paper/2212.04688/) (2022)
- [Quantifying the Effectiveness of Student Organization Activities using Natural Language Processing](https://mlsanity.com/paper/2408.08694/) (2024)

## Abstract (arXiv)

With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics,have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide a research on twitter data streams.General challenges and applications of Sentiment Analysis on Twitter are also discussed in this paper.