Leveraging feature-level fusion representations and attentional bidirectional RNN-CNN deep models for Arabic affect analysis on Twitter

Elfaik, Hanane; Nfaoui, El Habib

doi:10.1016/j.jksuci.2022.12.015

Cited by 8 publications

(13 citation statements)

References 63 publications

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“…In the evaluation of the proposed crime hotspot detection and prediction methodology, several key performance metrics are utilized to assess the effectiveness and reliability of the model. These metrics encompass accuracy, precision, recall, Fscore, and the area under the receiver operating characteristic curve (AUC-ROC) [39][40][41][42][43], each providing valuable insights into different aspects of the model's performance.…”

Section: B Evaluation Parametersmentioning

confidence: 99%

Leveraging Machine Learning Methods for Crime Analysis in Textual Data

Mussiraliyeva,

Baispay

2024

IJACSA

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The proposed research paper explores the application of machine learning techniques in crime analysis problem, specifically focusing on the classification of crimerelated textual data. Through a comparative analysis of various machine learning models, including traditional approaches and deep learning architectures, the study evaluates their effectiveness in accurately detecting and categorizing crimerelated text data. The performance of the models is assessed using rigorous evaluation metrics, such as the area under the receiver operating characteristic curve (AUC-ROC), to provide insights into their discriminative power and reliability. The findings reveal that machine learning frameworks, particularly the deep learning model, consistently outperform conventional machine learning approaches, highlighting the potential of advanced neural network architectures in crime analysis tasks. The implications of these findings for law enforcement agencies and researchers are discussed, emphasizing the importance of leveraging advanced machine learning techniques to enhance crime prevention and intervention efforts. Furthermore, avenues for future research are identified, including the integration of multiple data sources and the exploration of interpretability and explainability of machine learning models in crime analysis problem. Overall, this research contributes to advancing the field of crime analysis problem and underscores the importance of leveraging innovative computational approaches to address complex societal challenges.

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Section: B Evaluation Parametersmentioning

confidence: 99%

Leveraging Machine Learning Methods for Crime Analysis in Textual Data

Mussiraliyeva,

Baispay

2024

IJACSA

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“…Among the selected models, LDA, TF-IDF, SVM, CF, and MF emerged as the top five most frequently mentioned models, appearing in over 500 papers. It is important to note that while some recently gained substantial attention, such as BERT [34], CF [95], LSTM [96,97], DNN [98], and GRU [99,100], our study encompasses models from various time periods.…”

Section: Model Trendsmentioning

confidence: 99%

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Farshidi,

Rezaee,

Mazaheri

et al. 2023

Preprint

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Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user’s request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks.

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“…Normalisation : Word normalization means giving a format where some letters appear differently [19] for instance, , can appear in different forms like , and .…”

Section: B Pre-processingmentioning

confidence: 99%

“…Light stemmer : Light stemmer is one category of stemming approach that aims to reduce words to their stem by means of removing the most frequent word's prefix and/or suffix [20], [21], [19] .…”

Section: Stemmingmentioning

confidence: 99%

Investigate the Impact of Stemming on Mauritanian Dialect Classification using Machine Learning Techniques

CHRIF,

Seyed,

Mahmoud

et al. 2023

IJACSA

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Despite the plethora and diversity of research on Natural Language Processing (NLP). As a technique allowing computers to understand, generate, and manipulate human language; It still remains insufficient, especially with regard to the processing of Arabic texts and their dialects which are widely used. The proposed approach focuses on the application of machine learning techniques taking into account evaluation criteria such as training to comments expressed in Mauritanian dialect, published on social media notably Facebook, and compares results generated by three algorithms which we applied such as the Random Forest (RF), Naïve Bayes Multinominal (NBM), and Logistic Regression (LR) algorithm. Additionally, We then study the effect of machine learning techniques when different stemmers are combined with other features such as the tokenizers used to process the dataset. Although major challenges exist such as the morphology of Arabic is completely different from Latin letter languages, and there is no pre-existing dataset or dictionary to train the algorithms, the result we obtained after the experiments carried out on Weka shows that the RF and NBM algorithms are more efficient when applied with ArbicStemmerKhoja giving results respectively 96.37% and 71.40%; However, Logistic gets better performance results with Null Stemme is 81.65%. Results obtained by the three techniques applied with a light Arabic stemmer were more than 70%. This article presents a contribution to NLP based on Machine learning, descript also an important study that can determine the best Arabic classifier.

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Leveraging feature-level fusion representations and attentional bidirectional RNN-CNN deep models for Arabic affect analysis on Twitter

Cited by 8 publications

References 63 publications

Leveraging Machine Learning Methods for Crime Analysis in Textual Data

Leveraging Machine Learning Methods for Crime Analysis in Textual Data

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Investigate the Impact of Stemming on Mauritanian Dialect Classification using Machine Learning Techniques

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