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TweetNLP: Cutting-Edge Natural Language Processing for Social Media

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arxiv 2206.14774 v3 pith:3LRBIQCJ submitted 2022-06-29 cs.CL

Jose Camacho-Collados , Kiamehr Rezaee , Talayeh Riahi , Asahi Ushio , Daniel Loureiro , Dimosthenis Antypas , Joanne Boisson , Luis Espinosa-Anke
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Fangyu Liu Eugenio Martínez-Cámara Gonzalo Medina Thomas Buhrmann Leonardo Neves Francesco Barbieri
This is my paper
classification cs.CL
keywords socialmedialanguagetweetnlpmodelsanalysisintegratednatural
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

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  1. Speaking Beyond Language: A Large-Scale Multimodal Dataset for Learning Nonverbal Cues from Video-Grounded Dialogues

    cs.AI 2025-06 conditional novelty 6.0 of 10

    VENUS is a large podcast-derived dataset aligning text with 3D facial and body cues, and MARS is an LLM fine-tuned on it to generate both words and nonverbal tokens.

  2. AI in Mental Health: Emotional and Sentiment Analysis of Large Language Models' Responses to Depression, Anxiety, and Stress Queries

    cs.CL 2025-08 conditional novelty 5.0 of 10

    Eight LLMs show measurably different emotional tones in mental-health answers: anxiety prompts produced near-saturated fear scores, depression prompts the most sadness, and stress prompts the most optimism.

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