Sequential Models for Sentiment Analysis: A Comparative Study

Olaronke Oluwayemisi Adebanji, Irina Gelbukh, Hiram Calvo, Olumide Ebenezer Ojo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

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%.

Idioma originalInglés
Título de la publicación alojadaAdvances in Computational Intelligence - 21st Mexican International Conference on Artificial Intelligence, MICAI 2022, Proceedings
EditoresObdulia Pichardo Lagunas, Bella Martínez Seis, Juan Martínez-Miranda
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas227-235
Número de páginas9
ISBN (versión impresa)9783031194955
DOI
EstadoPublicada - 2022
Evento21st Mexican International Conference on Artificial Intelligence, MICAI 2022 - Monterrey, México
Duración: 24 oct. 202229 oct. 2022

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13613 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia21st Mexican International Conference on Artificial Intelligence, MICAI 2022
País/TerritorioMéxico
CiudadMonterrey
Período24/10/2229/10/22

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