A Primer on Opinion Mining: The Growing Research Area

Rani, Mikanshu; Singh, Jaswinder

doi:10.1007/978-981-15-5113-0_90

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…Sentiment analysis can be defined as the process of finding opinions that have positive, negative, and neutral polarities in data available on social media or websites (Rani & Singh, 2021). According to Bing Liu (2015), sentiment analysis is a field of computational study of opinions, sentiments, and emotions expressed through text.…”

Section: Introductionmentioning

confidence: 99%

Classification of Movie Review Sentiment Analysis Using Chi-Square and Multinomial Naïve Bayes with Adaptive Boosting

Hamzah

2021

J. Adv. Inf. Syst. Tech

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Sentiment analysis problems have attracted the attention of researchers. Sentiment analysis is a process that aims to determine the sentiment polarity of text. Nowadays, sentiment from product reviews has become a piece of important information for producers and potential customers. This paper conducted a sentiment analysis classification on a movie review from the IMDb site. In the classification analysis, the sentiment of movie reviews used the multinomial naïve Bayes algorithm. Adaboost was applied to boosting the accuracy of multinomial naïve Bayes. Feature selection is used to reduce the number of features and irrelevant features. The chi-square feature selection used was employed in the current study. The accuracy obtained in movie review sentiment analysis classification using the multinomial naïve Bayes algorithm is 81.39%. Meanwhile, the accuracy of the multinomial naïve Bayes algorithm by applying chi-square is 85.37%. The final result of multinomial naïve Bayes algorithm accuracy by applying AdaBoost and chi-square feature selection is 87.74%.

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Section: Introductionmentioning

confidence: 99%

Classification of Movie Review Sentiment Analysis Using Chi-Square and Multinomial Naïve Bayes with Adaptive Boosting

Hamzah

2021

J. Adv. Inf. Syst. Tech

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Leveraging the Automated Machine Learning for Arabic Opinion Mining: A Preliminary Study on AutoML Tools and Comparison to Human Performance

Garouani

Zaysa

2022

Digital Technologies and Applications

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A Study on Classification Techniques based on Opinions

Pooja

Singh

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Sentiment analysis or opinion mining is one of the considerable undertakings of Natural Language Processing. Sentiment analysis is a quick-growing investigation area in computer science. It includes a way of improvement for the collection and assessment of comments or opinions about legislation, laws, and opinions of products, and so forth, which are posted on the internet. Opinion mining analyzes the opinions given by people. In sentiment analysis, the customer feedback, review, shopping site review, online purchase reviews, politics opinions are considered for the analysis. Sentiment analysis is about detecting the emotions i.e. sad, happy, angry, excited, etc. The main objective of opinion mining is characterizing what the public think and comment. This review paper features certain discussions concerning the opinion-based classification and used approaches for the extraction of feeling assessment and opinion mining. Paper presents a comparison between different classification techniques. Applications and challenges of opinion mining are also discussed in this paper.

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A Primer on Opinion Mining: The Growing Research Area

Cited by 4 publications

References 39 publications

Classification of Movie Review Sentiment Analysis Using Chi-Square and Multinomial Naïve Bayes with Adaptive Boosting

Classification of Movie Review Sentiment Analysis Using Chi-Square and Multinomial Naïve Bayes with Adaptive Boosting

Leveraging the Automated Machine Learning for Arabic Opinion Mining: A Preliminary Study on AutoML Tools and Comparison to Human Performance

A Study on Classification Techniques based on Opinions

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