Sentiment Analysis on Twitter

Kamyab, Marjan; Tao, Ran; Mohammadi, Mohammad; Rasool, Abdur

doi:10.1145/3293663.3293687

Cited by 16 publications

(8 citation statements)

References 23 publications

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“…In this work, we provide a solution to tackle the problems below for the breast cancer dataset, which we found from [ 16 , 17 , 18 , 19 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…It involves the three different types of the experiment: KNN without feature selection, with linear SVM, and with Chi-square-based features. It indicated that the third technique, Chi-square-based feature selection, succeeded in accomplishing the highest accuracy on both datasets with Manhattan or Canberra distance functions [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

“…Apart from these advantageous accuracies with different classifiers and methods, these studies mentioned above have not considered the data exploratory techniques, which enable the data mining techniques to be more robust to acquire efficient performance. Due to the absence of such essential techniques, various studies [ 16 , 17 , 18 , 19 ] face the accuracy limitation of ML classifiers. Meanwhile, the confusion matrices misdiagnosed the malignant and benign classes in those studies due to the incorrect prediction of true negative and false negative matrices.…”

Section: Introductionmentioning

confidence: 99%

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Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Rasool

Bunterngchit

Luo

et al. 2022

IJERPH

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Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

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“…In this work, we provide a solution to tackle the problems below for the breast cancer dataset, which we found from [ 16 , 17 , 18 , 19 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Rasool

Bunterngchit

Luo

et al. 2022

IJERPH

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“…The TF-IDF score indicates a term's relative significance in the document and throughout the whole corpus. The TF-IDF score is made up of two terms: the first computes the normalized Term Frequency (TF), and the second computes the Inverse Document Frequency (IDF), which is calculated as the logarithm of the number of documents in the corpus divided by the number of documents in which the specific term appears [25,50].…”

Section: Character-level Featuresmentioning

confidence: 99%

AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Aslam,

Manzoor

et al. 2024

Symmetry

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The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model’s exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.

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“…Aspek politik serta ekonomi warga mereka menjadi bahan perdebatan di antara banyak pengguna. Web media sosial yang kerap digunakan untuk memperdebatkan isu -isu politik serta sosial adalah Twitter [1]. Twitter merupakan website microblogging yang terkenal di mana pengguna bisa mempublikasikan pembaruan status (diucap "tweet").…”

Section: A Pendahuluanunclassified

Analisis Sentimen Pengguna Sosial Media Twitter Terhadap Perokok Di Indonesia

Cahyono¹,

Setiyawati²

2023

ijcs

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One of the tools web users use to access, share, and discuss subjects of interest is social media. One social networking site, Twitter, is often used in real time to communicate this. Due to its significant negative impacts on both health and the economy, smoking is still a topic of regular debate and debate in Indonesia. This research was conducted to assess sentiment towards smokers and differentiate between positive and negative emotions. The data used in this study were obtained by crawling the Twitter social media network. Three Bayes techniques (NB), Support Vector Machine (SVM), and Logistic Regression are used in this study. In this study, 40.25% of Twitter users agreed with the existence of smokers in Indonesia, while 59.74% disagreed. The Naive Bayes method was used in this study, giving the highest accuracy value = 62.1% using 60% training data and 40% test data.

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Sentiment Analysis on Twitter

Cited by 16 publications

References 23 publications

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Analisis Sentimen Pengguna Sosial Media Twitter Terhadap Perokok Di Indonesia

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