Sentiment analysis in tweets: an assessment study from classical to modern word representation models

@article{Barreto2021SentimentAI,
  title={Sentiment analysis in tweets: an assessment study from classical to modern word representation models},
  author={S'ergio Barreto and Ricardo Moura and Jonnathan Carvalho and A. Paes and Alexandre Plastino},
  journal={Data Mining and Knowledge Discovery},
  year={2021},
  volume={37},
  pages={318 - 380}
}
With the exponential growth of social media networks, such as Twitter, plenty of user-generated data emerge daily. The short texts published on Twitter – the tweets – have earned significant attention as a rich source of information to guide many decision-making processes. However, their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks, including sentiment analysis. Sentiment classification is tackled… 
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3 Citations
Background Citations
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Methods Citations
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Results Citations
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3 Citations

A Resource-optimized and Accelerated Sentiment Analysis Method using Serverless Computing

Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble

A Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features is proposed that achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques.

Enriching datasets for sentiment analysis in tweets with instance selection

Different strategies for selecting instances from a set of labeled source datasets in order to improve the performance of classifiers trained only with the target dataset are proposed, including similarity metrics and variations in the number of selected instances.

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