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Analysis of Machine Learning Algorithms for Opinion Mining in Different Domains

Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
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Mach. Learn. Knowl. Extr. 2019, 1(1), 224-234; https://doi.org/10.3390/make1010014
Received: 1 November 2018 / Revised: 29 November 2018 / Accepted: 6 December 2018 / Published: 8 December 2018
(This article belongs to the Section Learning)
Sentiment classification (SC) is a reference to the task of sentiment analysis (SA), which is a subfield of natural language processing (NLP) and is used to decide whether textual content implies a positive or negative review. This research focuses on the various machine learning (ML) algorithms which are utilized in the analyzation of sentiments and in the mining of reviews in different datasets. Overall, an SC task consists of two phases. The first phase deals with feature extraction (FE). Three different FE algorithms are applied in this research. The second phase covers the classification of the reviews by using various ML algorithms. These are Naïve Bayes (NB), Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Passive Aggressive (PA), Maximum Entropy (ME), Adaptive Boosting (AdaBoost), Multinomial NB (MNB), Bernoulli NB (BNB), Ridge Regression (RR) and Logistic Regression (LR). The performance of PA with a unigram is the best among other algorithms for all used datasets (IMDB, Cornell Movies, Amazon and Twitter) and provides values that range from 87% to 99.96% for all evaluation metrics. View Full-Text
Keywords: sentiment classification; machine learning; sentiment analysis; feature extraction; natural language processing; opinion mining; computational intelligence sentiment classification; machine learning; sentiment analysis; feature extraction; natural language processing; opinion mining; computational intelligence
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Gamal, D.; Alfonse, M.; M. El-Horbaty, E.-S.; M. Salem, A.-B. Analysis of Machine Learning Algorithms for Opinion Mining in Different Domains. Mach. Learn. Knowl. Extr. 2019, 1, 224-234.

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