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

Peralta, Billy; Tirapegui, Victor; Pieringer, Christian; Alberto, Luis

doi:10.1007/978-3-030-33904-3_14

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…HMMs have become increasingly popular, as models for analyzing sequences and recognizing patterns. 6,10,30 The reason they are so valuable is their ability to handle systems that follow Markov processes with states. In sentiment analysis HMMs effectively capture emotions throughout sequences of sentences or documents.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, recent research has showcased the use of the Hidden Markov Model (HMM) in sentiment analysis particularly when it comes to modeling word order. HMMs have become increasingly popular, as models for analyzing sequences and recognizing patterns 6,10,30 . The reason they are so valuable is their ability to handle systems that follow Markov processes with states.…”

Section: Related Workmentioning

confidence: 99%

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Sentiment analysis incorporating convolutional neural network into hidden Markov model

Najafabadi

2024

Computational Intelligence

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The analysis of sentiments and mining of opinions have become more and more important in years because of the development of social media technologies. The methods that utilize natural language processing and lexicon‐based sentiment analysis techniques to analyze people's opinions in texts require the proper extraction of sentiment words to ensure accuracy. The current issue is tackled with a novel perspective in this paper by introducing a hybrid sentiment analysis technique. This technique brings together Convolutional Neural Network (CNN) and Hidden Markov Models (HMMs), to accurately categorize text data and pinpoint feelings. The proposed method involves 1D convolutional‐layer CNN to extract hidden features from comments and applying HMMs on a feature‐sentence matrix, allowing for the utilization of word sequences in extracting opinions. The method effectively captures diverse text patterns by extracting a range of features from texts using CNN. Text patterns are learned using text HMM by calculating the probabilities between sequences of feature vectors and clustering feature vectors. The paper's experimental evaluation employs benchmark datasets such as CR, MR, Subj, and SST2, demonstrating that the proposed method surpasses existing sentiment analysis techniques and traditional HMMs. One of its strengths is to analyze a range of text patterns and identify crucial features that recognize the emotion of different pieces of a sentence. Additionally, the research findings highlight the improved performance of sentiment analysis tasks through the strategic use of zero padding in conjunction with the masking technique.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sentiment analysis incorporating convolutional neural network into hidden Markov model

Najafabadi

2024

Computational Intelligence

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“…Sentiment analysis is an ever-growing research area, often focused on human interaction and social media. In this context, Peralta [9] uses a Markov chain approach to achieve good results on movie review classification. The work of Shinha [10] used labeled news to determine the sentiment passed by the text: positive, negative, or neutral.…”

Section: Related Workmentioning

confidence: 99%

Automatic evaluation of scientific abstracts through natural language processing

Lopes¹,

Vieira²,

Lira³

2021

Preprint

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This work presents a framework to classify and evaluate distinct research abstract texts which are focused on the description of processes and their applications. In this context, this paper proposes natural language processing algorithms to classify, segment and evaluate the results of scientific work. Initially, the proposed framework categorize the abstract texts into according to the problems intended to be solved by employing a text classification approach. Then, the abstract text is segmented into problem description, methodology and results. Finally, the methodology of the abstract is ranked based on the sentiment analysis of its results. The proposed framework allows us to quickly rank the best methods to solve specific problems. To validate the proposed framework, oil production anomaly abstracts were experimented and achieved promising results.

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

Cited by 2 publications

References 7 publications

Sentiment analysis incorporating convolutional neural network into hidden Markov model

Sentiment analysis incorporating convolutional neural network into hidden Markov model

Automatic evaluation of scientific abstracts through natural language processing

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