A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

Gaye, Babacar; Zhang, Dezheng; Wulamu, Aziguli

doi:10.3390/info12090374

Cited by 38 publications

(16 citation statements)

References 42 publications

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“…The adaptative boosting classifier (ADA) is another ensemble algorithm that uses a boosting technique for constructing a strong classifier, combining weak classifiers [ 44 ], and according to Sharef et al [ 45 ], the function with the linear combination of these classifiers is given by Equation (5):

…”

Section: Methodsmentioning

confidence: 99%

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Carvalho

Nepomuceno²,

Poleto³

et al. 2022

TropicalMed

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This article presents a study that applied opinion analysis about COVID-19 immunization in Brazil. An initial set of 143,615 tweets was collected containing 49,477 pro- and 44,643 anti-vaccination and 49,495 neutral posts. Supervised classifiers (multinomial naïve Bayes, logistic regression, linear support vector machines, random forests, adaptative boosting, and multilayer perceptron) were tested, and multinomial naïve Bayes, which had the best trade-off between overfitting and correctness, was selected to classify a second set containing 221,884 unclassified tweets. A timeline with the classified tweets was constructed, helping to identify dates with peaks in each polarity and search for events that may have caused the peaks, providing methodological assistance in combating sources of misinformation linked to the spread of anti-vaccination opinion.

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…”

Section: Methodsmentioning

confidence: 99%

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Carvalho

Nepomuceno²,

Poleto³

et al. 2022

TropicalMed

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show abstract

“…Gaye, B. et al [13] proposed a stacked ensemble model involving three stacked LSTM models. The output of this stacked approach was fed into a Logistic Regression (LR) classi er.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Using Multinomial LR-LSTM Model

Rani,

Boora,

Kumar

et al. 2023

Preprint

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Sentiment analysis (SA) refers to a technique utilized to ascertain the emotional state conveyed in information or text. It involves categorizing the text into three classes: positive, negative, or neutral. For instance, when someone says "the aqi of the city is good," they are expressing a positive opinion about the aqi of a specific place, while the statement "the aqi is bad" reflects the opposite. The introduction of social media increased the amount of content on the internet of sentiment data. Users on various social media platforms have been able to offer their opinions on various products, services, etc. These opinions are often expressed on social media in the form of movie reviews, product reviews, user comments, posts, etc. In light of this context, one of the captivating research areas in Natural Language Processing (NLP) is Twitter sentiment analysis. The paper proposes a stacked Multinomial-LR-LSTM model for the classification of tweets into three classes. Tweets are re-annotated using Text Blob. Twitter Sentiment dataset was used for experiments with accuracy of 97%.

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“…This makes preprocessing of the data a very important step for improving the performance of the ML models and reducing the training time [32,34,35]. In addition, the size of the featured set can be reduced from 50% to 30%, as stated by the authors of [36].…”

Section: Data Preprocessingmentioning

confidence: 99%

An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

et al. 2022

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Analyzing the sentiment of Arabic texts is still a big research challenge due to the special characteristics and complexity of the Arabic language. Few studies have been conducted on Arabic sentiment analysis (ASA) compared to English or other Latin languages. In addition, most of the existing studies on ASA analyzed datasets collected from Twitter. However, little attention was given to the huge amounts of reviews for governmental or commercial mobile applications on Google Play or the App Store. For instance, the government of Saudi Arabia developed several mobile applications in healthcare, education, and other sectors as a response to the COVID-19 pandemic. To address this gap, this paper aims to analyze the users’ opinions of six applications in the healthcare sector. An improved sentiment classification approach was proposed for measuring user satisfaction toward governmental services’ mobile apps using machine learning models with different preprocessing methods. The Arb-AppsReview dataset was collected from the reviews of these six mobile applications available on Google Play and the App Store, which includes 51k reviews. Then, several feature engineering approaches were applied, which include Bing Liu lexicon, AFINN, and MPQA Subjectivity Lexicon, bag of words (BoW), term frequency-inverse document frequency (TF-IDF), and the Google pre-trained Word2Vec. Additionally, the SMOTE technique was applied as a balancing technique on this dataset. Then, five ML models were applied to classify the sentiment opinions. The experimental results showed that the highest accuracy score (94.38%) was obtained by applying a support vector machine (SVM) using the SMOTE technique with all concatenated features.

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A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

Cited by 38 publications

References 42 publications

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Sentiment Analysis Using Multinomial LR-LSTM Model

An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

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