| Challenge: | Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need it. |
| Approach: | They propose deep learning models to predict optimism and pessimism in Twitter . they also show that a sentiment classifier would not be sufficient for predicting optimism and psi . |
| Outcome: | The proposed models outperform traditional machine learning classifiers on optimism and pessimism in Twitter. |
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| Challenge: | a recent study has established sentiment analysis as an alluring problem, but many feelings are left unexplored. |
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| Challenge: | Existing methods to analyze tweets are based on lexical features and a multi-channel convolutional neural architecture. |
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| Challenge: | Social media based micro-blogging sites like Twitter are used for expressing emotions and opinions. |
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| Challenge: | Existing annotated corpus of Reddit comments is limited by available annotation methods. |
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| Challenge: | Existing methods for sarcasm target detection are difficult to implement in natural language processing. |
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| Challenge: | opinion mining is a popular natural language processing technique, but a problem is robustness for user-generated texts . a recent study shows that a model that handles context can extract the opinion target with 90% accuracy . |
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Detecting Sexism in Tweets: A Sentiment Analysis and Graph Neural Network Approach (2025.naacl-srw)
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Diana P. Madera-Espíndola, Zoe Caballero-Domínguez, Valeria J. Ramírez-Macías, Sabur Butt, Hector Ceballos
| Challenge: | a new tool to detect sexism on social media platforms is being developed to identify such behavior . sexist ideologies such as sextism and gender-based violence can be spread through social media . |
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Predicting the Topical Stance and Political Leaning of Media using Tweets (2020.acl-main)
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| Challenge: | Existing methods for determining stances of media outlets and influential people are expensive. |
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Affect inTweets: A Transfer Learning Approach (2020.lrec-1)
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| Challenge: | Existing machine learning models require considerable effort to design task specific features to understand affectual states of people. |
| Approach: | They propose a transfer-learning based approach to infer the affectual state of a person from tweets. |
| Outcome: | The proposed model ranks 2nd, 4th and 6th in four of the four subtasks on SemEval-2018 task 1: Affect in Tweets. |