# TweetNLP: Cutting-Edge Natural Language Processing for Social Media

arXiv: https://arxiv.org/abs/2206.14774 · HTML: https://mlsanity.com/paper/2206.14774/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 56 references that are in the map.
- First posted on arXiv 29 Jun 2022.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders](https://mlsanity.com/paper/2104.08027/) (2021)
- [Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research](https://mlsanity.com/paper/2005.00357/) (2020)
- [Building a Sentiment Corpus of Tweets in Brazilian Portuguese](https://mlsanity.com/paper/1712.08917/) (2017)
- [Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis](https://mlsanity.com/paper/2201.07281/) (2022)

## Built on by (most cited first, 30 of 71)

- [Human Feedback is not Gold Standard](https://mlsanity.com/paper/2309.16349/) (2023)
- [Controlled Text Generation via Language Model Arithmetic](https://mlsanity.com/paper/2311.14479/) (2023)
- [Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems](https://mlsanity.com/paper/2310.05280/) (2023)
- [OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs](https://mlsanity.com/paper/2309.03876/) (2023)
- [Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts](https://mlsanity.com/paper/2210.03797/) (2022)
- [Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation](https://mlsanity.com/paper/2307.01680/) (2023)
- [Twitter Topic Classification](https://mlsanity.com/paper/2209.09824/) (2022)
- [LiSeCo: Linear Semantic Control for Language Generation](https://mlsanity.com/paper/2405.15454/) (2024)
- [LLaMAs Have Feelings Too: Unveiling Sentiment and Emotion Representations in LLaMA Models Through Probing](https://mlsanity.com/paper/2505.16491/) (2025)
- [Leveraging Implicit Feedback from Deployment Data in Dialogue](https://mlsanity.com/paper/2307.14117/) (2023)
- [CoBRA: Programming Cognitive Bias in Social Agents Using Classic Social Science Experiments](https://mlsanity.com/paper/2509.13588/) (2025)
- [Self-interpreting Adversarial Images](https://mlsanity.com/paper/2407.08970/) (2024)
- [Continuous Output Personality Detection Models via Mixed Strategy Training](https://mlsanity.com/paper/2406.16223/) (2024)
- [Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2504.15848/) (2025)
- [Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants](https://mlsanity.com/paper/2410.12891/) (2024)
- [A Group Fairness Lens for Large Language Models](https://mlsanity.com/paper/2312.15478/) (2023)
- [Using LLMs to Establish Implicit User Sentiment of Software Desirability](https://mlsanity.com/paper/2408.01527/) (2024)
- [Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models](https://mlsanity.com/paper/2409.17990/) (2024)
- [Speaking Beyond Language: A Large-Scale Multimodal Dataset for Learning Nonverbal Cues from Video-Grounded Dialogues](https://mlsanity.com/paper/2506.00958/) (2025)
- [Federated Learning Based Multilingual Emoji Prediction In Clean and Attack Scenarios](https://mlsanity.com/paper/2304.01005/) (2023)
- [Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter](https://mlsanity.com/paper/2308.02142/) (2023)
- [ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction](https://mlsanity.com/paper/2310.18706/) (2023)
- [Automatic Essay Multi-dimensional Scoring with Fine-tuning and Multiple Regression](https://mlsanity.com/paper/2406.01198/) (2024)
- [Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation](https://mlsanity.com/paper/2409.00696/) (2024)
- [RedditESS: A Mental Health Social Support Interaction Dataset -- Understanding Effective Social Support to Refine AI-Driven Support Tools](https://mlsanity.com/paper/2503.21888/) (2025)
- [On the Impact of Language Nuances on Sentiment Analysis with Large Language Models: Paraphrasing, Sarcasm, and Emojis](https://mlsanity.com/paper/2504.05603/) (2025)
- [AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries](https://mlsanity.com/paper/2508.11285/) (2025)
- [ReviewGuard: Enhancing Deficient Peer Review Detection via LLM-Driven Data Augmentation](https://mlsanity.com/paper/2510.16549/) (2025)
- [The Emotions of the Crowd: Learning Image Sentiment from Tweets via Cross-modal Distillation](https://mlsanity.com/paper/2304.14942/) (2023)
- [Non-Compositionality in Sentiment: New Data and Analyses](https://mlsanity.com/paper/2310.20656/) (2023)

## Abstract (arXiv)

In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. Task-specific systems are powered by reasonably-sized Transformer-based language models specialized on social media text (in particular, Twitter) which can be run without the need for dedicated hardware or cloud services. The main contributions of TweetNLP are: (1) an integrated Python library for a modern toolkit supporting social media analysis using our various task-specific models adapted to the social domain; (2) an interactive online demo for codeless experimentation using our models; and (3) a tutorial covering a wide variety of typical social media applications.