Sentiment Classification Using N-Gram Inverse Document Frequency and Automated Machine Learning

Maipradit, Rungroj; Hata, Hideaki; Matsumoto, Kenichi

doi:10.1109/ms.2019.2919573

Cited by 30 publications

(13 citation statements)

References 12 publications

(19 reference statements)

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“…When third experiment was performed using 3W3DT, NB outperforms with second fusion model with high accuracy among all. After that, they compared their best model 3W3DT-NB with already existing model, and proposed model leads with accuracy of 88.36% [44] presented general machine learning method with n -gram IDF feature extraction. After feature extraction, an automated ML tool was used for distribution of data according to sentiments.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches

Bangyal

Qasim

Rehman

et al. 2021

Computational and Mathematical Methods in Medicine

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A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K -nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

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Section: Literature Reviewmentioning

confidence: 99%

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches

Bangyal

Qasim

Rehman

et al. 2021

Computational and Mathematical Methods in Medicine

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“…In a subsequent study, Novielli et al [42] found that unsupervised sentiment tools for SE, such as SentistrengthSE work better than supervised tools when applied in a cross-platform setting. Maipradit et al [36] reported superior performance using n-grams in shallow learning models, like SVM, in multiple datasets. However, it is not clear which value of 𝑛 in the n-grams provided the best result.…”

Section: Studying Sentiments Se Repositories/scenariosmentioning

confidence: 99%

An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets

Uddin¹,

Guéhéneuc²,

Khomh³

et al. 2021

Preprint

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Sentiment analysis in software engineering (SE) has shown promise to analyze and support diverse development activities. Recently, several tools are proposed to detect sentiments in software artifacts. While the tools improve accuracy over off-the-shelf tools, recent research shows that their performance could still be unsatisfactory. A more accurate sentiment detector for SE can help reduce noise in analysis of software scenarios where sentiment analysis is required. Recently, combinations, i.e., hybrids of stand-alone classifiers are found to offer better performance than the stand-alone classifiers for fault detection. However, we are aware of no such approach for sentiment detection for software artifacts. We report the results of an empirical study that we conducted to determine the feasibility of developing an ensemble engine by combining the polarity labels of stand-alone SE-specific sentiment detectors. Our study has two phases. In the first phase, we pick five SE-specific sentiment detection tools from two recently published papers by Lin et al. [31,32], who first reported negative results with stand alone sentiment detectors and then proposed an improved SE-specific sentiment detector, POME [31]. We report the study results on 17,581 units (sentences/documents) coming from six currently available sentiment benchmarks for software engineering. We find that the existing tools can be complementary to each other in 85-95% of the cases, i.e., one is wrong but another is right. However, a majority voting-based ensemble of those tools fails to improve the accuracy of sentiment detection. We develop Sentisead, a supervised tool by combining the polarity labels and bag of words as features. Sentisead improves the performance (F1-score) of the individual tools by 4% (over Senti4SD [5]) -100% (over POME [31]). The initial development of Sentisead occurred before we observed the use of deep learning models for SE-specific sentiment detection. In particular, recent papers show the superiority of advanced language-based pre-trained transformer models (PTM) over rule-based and shallow learning models. Consequently, in a second phase, we compare and improve Sentisead infrastructure using the PTMs. We find that a Sentisead infrastructure with RoBERTa as the ensemble of the five stand-alone rule-based and shallow learning SE-specific tools from Lin et al. [31,32] offers the best F1-score of 0.805 across the six datasets, while a stand-alone RoBERTa shows an F1-score of 0.801.

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“…In Maipradit et al [13], a group of researchers suggests a method for classifying sentiments with a general framework for machine learning. n-gram IDF has been used in feature generation and selection stage.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Using Hybrid Feature Selection Techniques

Abdulkhaliq

Darwesh

2020

UHD J SCI TECH

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Nowadays, people from every part of the world use social media and social networks to express their feelings toward different topics and aspects. One of the trendiest social media is Twitter, which is a microblogging website that provides a platform for its users to share their views and feelings about products, services, events, etc., in public. Which makes Twitter one of the most valuable sources for collecting and analyzing data by researchers and developers to reveal people sentiment about different topics and services, such as products of commercial companies, services, well-known people such as politicians and athletes, through classifying those sentiments into positive and negative. Classification of people sentiment could be automated through using machine learning algorithms and could be enhanced through using appropriate feature selection methods. We collected most recent tweets about (Amazon, Trump, Chelsea FC, CR7) using Twitter-Application Programming Interface and assigned sentiment score using lexicon rule-based approach, then proposed a machine learning model to improve classification accuracy through using hybrid feature selection method, namely, filter-based feature selection method Chi-square (Chi-2) plus wrapper-based binary coordinate ascent (Chi-2 + BCA) to select optimal subset of features from term frequency-inverse document frequency (TF-IDF) generated features for classification through support vector machine (SVM), and Bag of words generated features for logistic regression (LR) classifiers using different n-gram ranges. After comparing the hybrid (Chi-2+BCA) method with (Chi-2) selected features, and also with the classifiers without feature subset selection, results show that the hybrid feature selection method increases classification accuracy in all cases. The maximum attained accuracy with LR is 86.55% using (1 + 2 + 3-g) range, with SVM is 85.575% using the unigram range, both in the CR7 dataset.

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Sentiment Classification Using N-Gram Inverse Document Frequency and Automated Machine Learning

Cited by 30 publications

References 12 publications

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches

An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets

Sentiment Analysis Using Hybrid Feature Selection Techniques

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