| Resumen |
Sentiment analysis has been a focus of study in Natural Language Processing (NLP) tasks in recent years. In this paper, we propose the task of analysing sentiments using five sequential models and we compare their performance on a Twitter dataset. We used the bag of words, as well as the tf-idf, and the Word2Vec embeddings, as input features to the models. The precision, recall, f1 and accuracy scores of the proposed models were used to evaluate the models’ performance. The Bi-LSTM model with Word2Vec embedding performs the best against the dataset, with an accuracy of 84%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
