| Challenge: | Sentiment analysis is a popular area of Natural Language Processing due to its subjective and semantic characteristics. |
| Approach: | They propose to annotate Brazilian Portuguese sentences manually using a sentiment corpus . they run experiments on polarity classification using six machine learning classifiers . |
| Outcome: | The proposed method is based on a Brazilian Portuguese sentiment corpus and achieved 80.38% on F-Measure and 64.87% when including the neutral class. |
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| Challenge: | In Brazil, hate speech is prohibited, however the regulation is not effective due to the difficulty of identifying, quantifying and classifying this kind of online content. |
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NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis (2022.lrec-1)
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Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Sebastian Ruder, Ibrahim Sa’id Ahmad, Idris Abdulmumin, Bello Shehu Bello, Monojit Choudhury, Chris Chinenye Emezue, Saheed Salahudeen Abdullahi, Anuoluwapo Aremu, Alípio Jorge, Pavel Brazdil
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Daniel Peñaloza, Rodrigo López, Juanjosé Tenorio, Héctor Gómez, Arturo Oncevay-Marcos, Marco A. Sobrevilla Cabezudo
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| Challenge: | a huge amount of content is being generated every day due to the pervasiveness of social media. |
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| Challenge: | a new study analyzes the nature of twitter data and compares it with other social networking websites. |
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| Challenge: | Existing sentiment analysis models are not available for Odia 1 as it is a resource-poor language. |
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An Italian Twitter Corpus of Hate Speech against Immigrants (L18-1)
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Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition (2020.lrec-1)
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