EmotionAlBERTo: Emotion Recognition of Italian Social Media Texts Through BERT

Chiorrini, Andrea; Diamantini, Claudia; Mircoli, Alex; Potena, Domenico; Storti, Emanuele

doi:10.1109/icpr56361.2022.9956403

Cited by 15 publications

(15 citation statements)

References 14 publications

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“…The researchers created unique classifiers for every task and used real-world tweet datasets to evaluate the models' efficacy. Impressive accuracy rates of 0.92 for sentiment analysis and 0.90 for emotion recognition were shown in the experiment results [27]. Instead of using a standard BERT tokenizer, a study suggested a method to integrate an Arabic BERT tokenizer.…”

Section: Related Workmentioning

confidence: 97%

An Improved Machine Learning-Driven Framework for Cryptocurrencies Price Prediction With Sentimental Cautioning

Zubair,

Ali,

Alhussein

et al. 2024

IEEE Access

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Cryptocurrencies, recognized by their extreme volatility due to dependency on multiple direct and indirect factors, offer a significant challenge regarding precise price forecasting. This uncertainty has led to investment hesitation within the digital currency market. Previous research attempts have presented methodologies for price forecasting and trend prediction in cryptocurrencies. However, these forecasts have typically suffered from increased error rates, leaving the opportunity for improvement in this field. Furthermore, the influence of sentiment-based factors could compromise the reliability of price predictions. In this research, we have proposed a machine learning-driven framework that provides precise cryptocurrency price projections and adds an alert mechanism to guide investors. Our fundamental analyzer, Bi-LSTM and GRU hybrid model use historical data of digital currencies to train and reliably anticipate future values. Complementing this, a sentiment analyzer, utilizing a BERT and VADER hybrid model, analyzes sentiments to assess the forecast price as trustworthy or uncertain. Besides assisting investor decision-making, this technique also helps risk management in the dynamic realm of cryptocurrency. Our suggested approach delivers highly precise price predictions with dramatically decreased error rates compared to prior competitive studies. The proposed Bi-LSTM-GRU-BERT-VADER (BLGBV) model is tested for three cryptocurrencies, namely BTC, ETH, and Dogecoin and reports an average root mean square error (RMSE) of 0.0241%, 0.0645%, and 0.0978%, respectively.

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Section: Related Workmentioning

confidence: 97%

An Improved Machine Learning-Driven Framework for Cryptocurrencies Price Prediction With Sentimental Cautioning

Zubair,

Ali,

Alhussein

et al. 2024

IEEE Access

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show abstract

“…Proses ini dilakukan pada tingkat kata dan karakter, memungkinkan model untuk memahami konteks dan hubungan antar-karakter. Metode ini membantu BERT mengatasi kata-kata yang sulit atau morfologi yang berbeda sambil mempertahankan struktur informasi yang penting (Chiorrini et al, 2021;Hutama & Suhartono, 2022;Karayiğit et al, 2022).…”

Section: Tokenizeunclassified

Mengungkap Opini Publik: Pendekatan BERT-based-caused untuk Analisis Sentimen pada Komentar Film

Mudding

2024

JSCE

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BERT (Bidirectional Encoder Representations from Transformers) adalah model bahasa yang revolusioner dalam pemrosesan bahasa alami, mengandalkan encoder untuk menghasilkan representasi kontekstual dari teks input. Melalui pendekatan tokenisasi, embedding, dan attention mechanisms pada setiap layer transformer, BERT memungkinkan pemahaman hubungan antar kata secara mendalam dan bidireksional. Keunikan BERT terletak pada kemampuannya untuk memproses konteks dari kedua arah, menciptakan representasi vektor yang kaya makna. Model ini telah menjadi pionir dalam transfer learning di NLP, memungkinkan pemanfaatan representasi umum pada tugas-tugas khusus setelah proses pelatihan. Dengan demikian, BERT mengubah paradigma pemrosesan bahasa alami, membuka pintu untuk aplikasi yang lebih canggih seperti klasifikasi teks, analisis sentimen, dan pemahaman bahasa yang lebih kontekstual.

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“…In this context, scholars continue to explore more innovative and effective methods. Representative research cases include the following: Paranjape et al used Longformer, BERT, and BigBird models for emotion classification and combined these with a threshold voting mechanism to derive the final results [10]; Chiorrini et al utilized a BERT-based emotion classifier network for the sentiment classification of tweet data [11]; additionally, Chaudhari et al employed a vision transformer for facial emotion recognition [12]. However, these works only use a single data source for emotion recognition and lack multimodal data sources.…”

Section: Emotion Analysis Research On Spoken Languagementioning

confidence: 99%

Integrating Large Language Models (LLMs) and Deep Representations of Emotional Features for the Recognition and Evaluation of Emotions in Spoken English

Wang,

Yang,

Wang

et al. 2024

Applied Sciences

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This study is dedicated to developing an innovative method for evaluating spoken English by integrating large language models (LLMs) with effective space learning, focusing on the analysis and evaluation of emotional features in spoken language. Addressing the limitation of current spoken English evaluation software that primarily focuses on acoustic features of speech (such as pronunciation, frequency, and prosody) while neglecting emotional expression, this paper proposes a method capable of deeply recognizing and evaluating emotional features in speech. The core of the method comprises three main parts: (1) the creation of a comprehensive spoken English emotion evaluation dataset combining emotionally rich speech data synthesized using LLMs with the IEMOCAP dataset and student spoken audio; (2) an emotion feature encoding network based on transformer architecture, dedicated to extracting effective spatial features from audio; (3) an emotion evaluation network for the spoken English language that accurately identifies emotions expressed by Chinese students by analyzing different audio characteristics. By decoupling emotional features from other sound characteristics in spoken English, this study achieves automated emotional evaluation. This method not only provides Chinese students with the opportunity to improve their ability to express emotions in spoken English but also opens new research directions in the fields of spoken English teaching and emotional expression evaluation.

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EmotionAlBERTo: Emotion Recognition of Italian Social Media Texts Through BERT

Cited by 15 publications

References 14 publications

An Improved Machine Learning-Driven Framework for Cryptocurrencies Price Prediction With Sentimental Cautioning

An Improved Machine Learning-Driven Framework for Cryptocurrencies Price Prediction With Sentimental Cautioning

Mengungkap Opini Publik: Pendekatan BERT-based-caused untuk Analisis Sentimen pada Komentar Film

Integrating Large Language Models (LLMs) and Deep Representations of Emotional Features for the Recognition and Evaluation of Emotions in Spoken English

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