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Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Journal of Statistics and Management Systems
Open Access Research Article

A pragmatic analysis of text-based sentiment analysis models from an analytical perspective

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pp. 285–294Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1254XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1254
Pages:
285–294

Abstract

NLP sentiment analysis involves pre-processing, feature extraction, feature selection, sentiment classification, and post-processing. Pre-processing can include PoS tagging, stop-word removal, language-specific missing work detection and correction, etc. Word2vec, N Gramme, term frequency (TF), distance measures, etc. can extract and select features. CNNs, DNNs, and other machine learning and deep learning models can be used for classification and post-processing. These models form a sentiment analysis engine, which can be assessed for accuracy, precision, recall, analysis delay, computational complexity, etc. The wide variation in algorithmic combination and performance makes it difficult for researchers to choose the best model(s) for their application. This task requires a lot of model testing and validation, which increases time-to-market and product cost. This text surveys recently proposed sentiment analysis models to reduce ambiguity and speed up system development. This survey characterises reviewed models’ nuances, advantages, limitations, and future research scopes to help readers choose the best models for their application deployment. These models’ delay, accuracy, precision, recall, computational complexity, and probable use are also compared in this text. This comparison lets readers choose the best model(s) for their application deployment and use them without validation. Reducing testing delay will improve model performance for system design and deployment for multiple sentiment analysis applications.

Keywords

SentimentAnalysisNatural languageProcessingDelayPrecisionComplexity

Subject Classifications

68Q30

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