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

Abstract

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 mainly by machine learning-based classifiers. The literature has adopted different types of word representation models to transform tweets to vector-based inputs to feed sentiment classifiers. The representations come from simple count-based methods, such as bag-of-words, to more sophisticated ones, such as BERTweet, built upon the trendy BERT architecture. Nevertheless, most studies mainly focus on evaluating those models using only a small number of datasets. Despite the progress made in recent years in language modeling, there is still a gap regarding a robust evaluation of induced embeddings applied to sentiment analysis on tweets. Furthermore, while fine-tuning the model from downstream tasks is prominent nowadays, less attention has been given

Development of Context-based Sentiment Classification for Intelligent Stock Market Prediction

“…[11] explores the complexities of automating tweet analysis, a crucial aspect for understanding the inherent challenges of sentiment analysis. Their findings further support [7]'s conclusions, underscoring the importance of a profound comprehension of natural text to effectively navigate the informal syntax characteristic of tweets.…”

“…The introduction of contextualized embedding has significantly influenced sentiment analysis, particularly for social media content such as tweets. The work of [7] stands out in this field; it assesses a range of word representation models, including Transformer-based auto-encoder models like RoBERTa, and showcases their effectiveness in capturing the intricacies of the informal and evolving language found in tweets. This research highlights the advantages of contextualized models over static ones for sentiment analysis, aligning with the findings of [8], [9], and [10].…”

Development of Context-Based Sentiment Classification for Intelligent Stock Market Prediction

“…The authors of [11] explored the complexities of automating tweet analysis, a crucial aspect of understanding the inherent challenges of sentiment analysis. Their findings further supports' [7], underscoring the importance of the profound comprehension of natural text to effectively navigate the informal syntax characteristic of tweets.…”

“…The introduction of contextualized embedding has significantly influenced sentiment analysis, particularly for social media content such as tweets. The work of [7] stands out in this field; it assesses a range of word representation models, including transformer-based auto-encoder models like RoBERTa, and showcases their effectiveness in capturing the intricacies of the informal and evolving language found in tweets. This research highlights the advantages of contextualized models over static ones for sentiment analysis, aligning with the findings of [8][9][10].…”

Análise de Sentimento de Postagens de Usuários no Twitter Combinando GPT-3 e Aprendizado de Máquina: Um Estudo de Caso Sobre o 2º Turno das Eleições Presidências Brasileiras

“…Normalmente, a tarefa da análise de sentimentos é reduzida a determinar se as emoc ¸ões carregam uma conotac ¸ão positiva ou negativa, ou como classificá-las. Um dos maiores desafios na classificac ¸ão do sentimento dos tweets é que as pessoas normalmente comunicam seus sentimentos e opiniões em um estilo linguístico casual, o que leva à presenc ¸a de palavras com erros ortográficos e ao uso descuidado da gramática [Barreto et al 2023].…”