A Simple Proposal for Sentiment Analysis on Movies Reviews with Hidden Markov Models

Billy Peralta, Victor Tirapegui, Christian Pieringer, Luis Caro

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

Resumen

Sentiment analysis of texts is the field of study which analyses and studies opinions, sentiments, value judgments, affections and emotions in texts like blogs, news and treating of products, organisations, events and topics. If information on subjective content is required, such as the emotion aroused by an event, computer techniques must be applied to analyse the pattern of public opinion. A common technique for analysing texts is the “Bag of Words”, which provides good results assuming that the words are independent of one another. In this work we propose the use of Hidden Markov Chains to determine the polarity of the opinions expressed on movie reviews. We propose a method for simulating hidden states through clustering techniques; we then carry out a sensitivity analysis of the model in which we apply variations to model parameters such as the number of hidden states or the number of words used. The results show that our proposal gives a 3% improvement over the basic model using F-score for real databases of public opinion.Sentiment analysis of texts is the field of study which analyses and studies opinions, sentiments, value judgments, affections and emotions in texts like blogs, news and treating of products, organisations, events and topics. If information on subjective content is required, such as the emotion aroused by an event, computer techniques must be applied to analyse the pattern of public opinion. A common technique for analysing texts is the “Bag of Words”, which provides good results assuming that the words are independent of one another. In this work we propose the use of Hidden Markov Chains to determine the polarity of the opinions expressed on movie reviews. We propose a method for simulating hidden states through clustering techniques; we then carry out a sensitivity analysis of the model in which we apply variations to model parameters such as the number of hidden states or the number of words used. The results show that our proposal gives a 3% improvement over the basic model using F-score for real databases of public opinion.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 24th Iberoamerican Congress, CIARP 2019, Proceedings
EditoresIngela Nyström, Yanio Hernández Heredia, Vladimir Milián Núñez
EditorialSpringer
Páginas152-162
Número de páginas11
ISBN (versión impresa)9783030339036
DOI
EstadoPublicada - 1 ene 2019
Evento24th Iberoamerican Congress on Pattern Recognition, CIARP 2019 - Havana, Cuba
Duración: 28 oct 201931 oct 2019

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11896 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia24th Iberoamerican Congress on Pattern Recognition, CIARP 2019
PaísCuba
CiudadHavana
Período28/10/1931/10/19

Áreas temáticas de ASJC Scopus

  • Ciencia computacional teórica
  • Informática (todo)

Huella Profundice en los temas de investigación de 'A Simple Proposal for Sentiment Analysis on Movies Reviews with Hidden Markov Models'. En conjunto forman una huella única.

  • Citar esto

    Peralta, B., Tirapegui, V., Pieringer, C., & Caro, L. (2019). A Simple Proposal for Sentiment Analysis on Movies Reviews with Hidden Markov Models. En I. Nyström, Y. Hernández Heredia, & V. Milián Núñez (Eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 24th Iberoamerican Congress, CIARP 2019, Proceedings (pp. 152-162). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11896 LNCS). Springer. https://doi.org/10.1007/978-3-030-33904-3_14