ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection

Pérez, Juan Manuel; Arango, Aymé; Luque, Franco M.

doi:10.18653/v1/2020.semeval-1.199

Cited by 8 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pre Trained LLM: After the preprocessing the dataset three deep pretrained models for spanish language were used: i) Py-sentimiento, a Robertuito and RoBERTa-based model trained in spanish tweets (48)(49)(50), ii) a customized version of VADER (Valence Aware Dictionary and sEntiment Reasoner) wich translate comments and classify the text polarity ( 51) and iii) Distilbert-based model, a small, fast and light Transformer by distilling BERT base (52,53).…”

Section: Methodsmentioning

confidence: 99%

Towards an improved of teaching practice using Sentiment Analysis in Student Evaluation

Peña-Torres

2024

inycomp

View full text Add to dashboard Cite

Student evaluation of teaching (SET) is an ad-hoc way of assessing teaching effectiveness in higher education institutions. In this paper, we present an approach to analyzing sentiments expressed in SET comments using a large language model (LLM). By employing natural language processing techniques, we extract and analyze sentiments expressed by students when the course has ended, aiming to provide educators and administrators with valuable insights into teaching quality and elements to improve teaching practice. Our study demonstrates the effectiveness of LLMs in sentiment analysis of comments, highlighting their potential to enhance the evaluation process. Our experiments with a crowdsourced tagged dataset show a 93% of accuracy in the classification of feedback messages. We discuss the implications of our findings for educational institutions and propose future directions for research in this domain.

show abstract

Section: Methodsmentioning

confidence: 99%

Towards an improved of teaching practice using Sentiment Analysis in Student Evaluation

Peña-Torres

2024

inycomp

View full text Add to dashboard Cite

show abstract

“…For each retrieved tweet, we analyzed the hate speech content using pysentimiento (Pérez et al, 2021), a Python toolkit providing advanced classification algorithms in opinion mining tasks including sentiment analysis, emotion detection, and hate speech detection. Powered by pre-trained language models for several languages (Cañete et al, 2020;Devlin et al, 2019;Pérez et al, 2022) and the the Transformer architecture (Vaswani et al, 2017), the models are executed and distributed through the huggingface library (Wolf et al, 2020).…”

Section: Hate Speech Classification Methodsmentioning

confidence: 99%

Gender biases and hate speech: promoters and targets in the Argentinean political context

Domenech,

Pérez,

Rosati

et al. 2023

Preprint

View full text Add to dashboard Cite

Hate speech found in social media a place to flourish. In the Argentinean context, new right wing parties have disrupted the political arena, winning the primary elections of 2023. Many of these new right-wing figures grew in popularity due to their use of social media, on a background of increasing political violence. In this article, we use quantitative and qualitative tools to investigate the prevalence of hate speech targeting women politicians and analyze the role of different political affiliations in promoting such discourse. Furthermore, we propose a model that predicts users' political alignments based on their profile descriptions, allowing us to explore the distribution of hate speech among different political orientations. Our results provide a descriptive account of the relationship between hate speech by politicians and other users, and shows that right-wing political figures and supporters are strong emissors of hate speech, while women, especially those from the left-wing are more prone to receive violent content.

show abstract

“…This adaptation is rooted in RoBERTa, an improved version of BERT distinguished by augmented data and extended training iterations. Pérez et al (2022) introduced Robertuito to enhance sentiment analysis within the Pysentimento framework. This comprehensive toolkit is designed to provide users with the capability to perform sentiment analysis on their proprietary data or make use of alternative models available in Pysentimento or the Hugging Face Transformers library.…”

Section: Pysentimentomentioning

confidence: 99%

Cryptocurrency Price Prediction: A Comparative Sentiment Analysis Approach Using SVM, CNN-LSTM, and Pysentimento during Times of Crisis

Rateb,

Alansary,

Elzouka

et al. 2024

Preprint

View full text Add to dashboard Cite

Sentiment analysis is a powerful tool for extracting valuable insights from social media data. In this paper, more than one million tweets spanning three months (March, June, and December 2022) regarding three cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) during the Russian-Ukrainian War are considered. Two models, a convolutional neural network with long short-term memory (CNN-LSTM) and a support vector machine (SVM) with GloVe and TF-IDF features, are trained on a labeled dataset of more than fifty thousand tweets about Bitcoin labeled as (positive, negative, and neutral). A pretrained model (Pysentimento) for sentiment analysis is also employed to compare the performances of the three models. The models are tested on the labeled dataset and then evaluated on the unlabeled tweets, revealing that Pysentimento's level of accuracy outperforms the other two models. Google Trends, along with the opening and closing prices, and the volume of the three cryptocurrencies, in addition to the results of Pysentimento sentiment classification, are employed to apply the Pearson correlation coefficient and conduct price prediction analysis using the SARIMA model. It is found that Bitcoin may appeal to those seeking stability and a known record of accomplishment, while Binance Coin and Ethereum may attract investors looking for more diverse opportunities. Sentiment analysis using machine learning is found to provide invaluable information for cryptocurrency price forecasting and trading strategies, especially in the context of geopolitical events and market volatility.

show abstract

ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection

Cited by 8 publications

References 22 publications

Towards an improved of teaching practice using Sentiment Analysis in Student Evaluation

Towards an improved of teaching practice using Sentiment Analysis in Student Evaluation

Gender biases and hate speech: promoters and targets in the Argentinean political context

Cryptocurrency Price Prediction: A Comparative Sentiment Analysis Approach Using SVM, CNN-LSTM, and Pysentimento during Times of Crisis

Contact Info

Product

Resources

About