Expressively vulgar: The socio-dynamics of vulgarity and its effects on sentiment analysis in social media (C18-1)
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| Challenge: | Vulgarity is a common linguistic expression and is used to perform several linguistic functions. |
| Approach: | They analyze vulgarity using tweets from users with known demographics and sentiment ratings for vulgar tweets to study sentiment analysis performance. |
| Outcome: | The proposed model can boost sentiment analysis performance by analyzing vulgar tweets and tweet sentiment ratings. |
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| Challenge: | Vulgar words are employed in language use for several different functions, including expressing aggression, signaling group identity or the informality of the communication. |
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| Challenge: | In this paper, we examine the problem of pejorative language, an under-explored topic in computational linguistics. |
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| Challenge: | Using computational models, the use of offensive language is pervasive in social media . a popular line of research is the study of machine learning classifiers to identify offensive content online . |
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Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media (2020.lrec-1)
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| Challenge: | Swearing is a common form of verbal communication and occurs in social media and online forums . a study by a team of researchers has investigated the phenomenon of swearing in Twitter . |
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The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis (2023.emnlp-main)
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Pranav Venkit, Mukund Srinath, Sanjana Gautam, Saranya Venkatraman, Vipul Gupta, Rebecca Passonneau, Shomir Wilson
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Semantics and Sentiment: Cross-lingual Variations in Emoji Use (2024.emnlp-main)
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| Challenge: | emojis have been used in social media for a decade but have been inconsistently used in contexts and in isolation. |
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Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics (2020.emnlp-main)
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| Challenge: | A large-scale dataset is collected from Chinese microblog Sina Weibo with over 13 thousand trending topics, emotion votes in 24 fine-grained types from massive participants, and user comments to allow context understanding. |
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EmoTag1200: Understanding the Association between Emojis and Emotions (2020.emnlp-main)
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| Challenge: | Emojis are increasingly used to convey affect, but their use is not trivial. |
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TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media (2022.coling-1)
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Daniel Loureiro, Aminette D’Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa-Anke, Leonardo Neves, Francesco Barbieri, Jose Camacho-Collados
| Challenge: | Language models are often clean and time-invariant, and do little to no account of social media usage. |
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Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)
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| Challenge: | linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks . |
| Approach: | They propose to use unsupervised, supervised, and finally supervised methods to extract emotional associations from pretrained vectors and models. |
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