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

Billy Peralta, Victor Tirapegui, Christian Pieringer, Luis Caro

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 24th Iberoamerican Congress, CIARP 2019, Proceedings
EditorsIngela Nyström, Yanio Hernández Heredia, Vladimir Milián Núñez
PublisherSpringer
Pages152-162
Number of pages11
ISBN (Print)9783030339036
DOIs
Publication statusPublished - 1 Jan 2019
Event24th Iberoamerican Congress on Pattern Recognition, CIARP 2019 - Havana, Cuba
Duration: 28 Oct 201931 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11896 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Iberoamerican Congress on Pattern Recognition, CIARP 2019
Country/TerritoryCuba
CityHavana
Period28/10/1931/10/19

Keywords

  • Clustering
  • Hidden Markov Models
  • Sentimental analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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