Textual Dataset Classification Using Supervised Machine Learning Techniques

Jaleel, Hanan; Stephan, Jane; Naji, Sinan A.

doi:10.30684/etj.v40i4.1970

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The input data are converted into a set of features at this stage via feature extraction [39]. TF-IDF technique extracts features in natural language processing, valuing word importance by weighting frequent, rare words [40]…”

Section: Tf-idf Feature Extractionmentioning

confidence: 99%

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

Abdalrdha,

Al-Bakry,

Farhan

2023

RIA

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As social media grows, recognizing and managing illicit content, including threats, harassment, hate speech, armed robbery, drug smuggling, blackmail, and other crimes, is crucial. The present study uses machine learning and deep learning to create an intelligent lexicon for identifying crime-related material in Twitter tweets. The Aho-Corasick technique effectively creates a dictionary for extensive text corpus search, categorization, and keyword-based action execution. This strategy overcomes the evolution of language dynamics and criminal vocabulary to improve crime-related information detection. This paper aims to gather accurate data to help law enforcement identify and prevent specific crimes. For tweet preprocessing and extraction of relevant information like textual patterns and other distinctive qualities, natural language processing (NLP) technologies are prioritized. The paper describes labeling tweets into crime categories. This dataset trains supervised learning models to categorize tweets as criminal or not. XGBoost and Hybrid CNN-LSTM are combined for this. The suggested technique is assessed using precision, recall, F1-Score, accuracy, and MAP accuracy measures. These metrics measure the model's crime-related tweet identification accuracy. The Arabic tweets dataset, encompassing 18493 tweets and 10 features, is utilized for model testing. After training, the Hybrid CNN-LSTM model demonstrated an accuracy of 99.84% and a macro F1-Score of 98.20%. When the XGBoost method was employed, the traditional machine learning model achieved a peak F1 macro score of 99.36% and a maximum accuracy of 100%. The results suggest that while the deep learning models outperform machine learning models in the F1-Score, XGBoost exhibits superior accuracy. The paper presents a comprehensive strategy for crime detection in tweets, potentially offering a significant tool for law enforcement agencies.

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Section: Tf-idf Feature Extractionmentioning

confidence: 99%

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

Abdalrdha,

Al-Bakry,

Farhan

2023

RIA

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Threatening URDU Language Detection from Tweets Using Machine Learning

et al. 2022

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Technology’s expansion has contributed to the rise in popularity of social media platforms. Twitter is one of the leading social media platforms that people use to share their opinions. Such opinions, sometimes, may contain threatening text, deliberately or non-deliberately, which can be disturbing for other users. Consequently, the detection of threatening content on social media is an important task. Contrary to high-resource languages like English, Dutch, and others that have several such approaches, the low-resource Urdu language does not have such a luxury. Therefore, this study presents an intelligent threatening language detection for the Urdu language. A stacking model is proposed that uses an extra tree (ET) classifier and Bayes theorem-based Bernoulli Naive Bayes (BNB) as the based learners while logistic regression (LR) is employed as the meta learner. A performance analysis is carried out by deploying a support vector classifier, ET, LR, BNB, fully connected network, convolutional neural network, long short-term memory, and gated recurrent unit. Experimental results indicate that the stacked model performs better than both machine learning and deep learning models. With 74.01% accuracy, 70.84% precision, 75.65% recall, and 73.99% F1 score, the model outperforms the existing benchmark study.

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Textual Dataset Classification Using Supervised Machine Learning Techniques

Cited by 2 publications

References 11 publications

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

Threatening URDU Language Detection from Tweets Using Machine Learning

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