QiBERT -- Classifying Online Conversations Messages with BERT as a Feature
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Abstract (arXiv)
Recent developments in online communication and their usage in everyday life have caused an explosion in the amount of a new genre of text data, short text. Thus, the need to classify this type of text based on its content has a significant implication in many areas. Online debates are no exception, once these provide access to information about opinions, positions and preferences of its users. This paper aims to use data obtained from online social conversations in Portuguese schools (short text) to observe behavioural trends and to see if students remain engaged in the discussion when stimulated. This project used the state of the art (SoA) Machine Learning (ML) algorithms and methods, through BERT based models to classify if utterances are in or out of the debate subject. Using SBERT embeddings as a feature, with supervised learning, the proposed model achieved results above 0.95 average accuracy for classifying online messages. Such improvements can help social scientists better understand human communication, behaviour, discussion and persuasion.
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References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
- 2019Text Classification Algorithms: A Surveycited by 163
- 2019Choosing Transfer Languages for Cross-Lingual Learningcited by 144
- 2014word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding methodcited by 112
- 2016Sequential Short-Text Classification with Recurrent and Convolutional Neural Networkscited by 28
- 2020Sarcasm Detection in Tweets with BERT and GloVe Embeddingscited by 8
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