Age Group Prediction on Textual Data using Sentiment Analysis

Yadav, Divakar; Gupta, Aarushi; Asati, Saumya; Choudhary, Nikhil; Yadav, Arun Kr

doi:10.1145/3439231.3439262

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…LSTM units enable the model to capture temporal dependencies and contextual information within textual content, while CNN layers effectively extract spatial features from textual and visual data. This combination facilitates a comprehensive analysis of social media posts, enhancing the model's ability to detect subtle nuances indicative of cyberbullying behavior [36]. Furthermore, the utilization of ensemble learning techniques allows for the aggregation of multiple LSTM-CNN hybrid models, mitigating the risk of overfitting and improving classification accuracy [37].…”

Section: Discussionmentioning

confidence: 99%

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Baiganova,

Toxanova,

Yerekesheva

et al. 2024

ijacsa

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With the burgeoning use of social media platforms, online harassment and cyberbullying have become significant concerns. Traditional mechanisms often falter, necessitating advanced methodologies for efficient detection. This study presents an innovative approach to identifying cyberbullying incidents on social media sites, employing a hybrid neural network architecture that amalgamates Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). By harnessing the sequential processing capabilities of LSTM to analyze the temporal progression of textual data, and the spatial discernment of CNN to pinpoint bullying keywords and patterns, the model demonstrates substantial improvement in detection accuracy compared to extant methods. A diverse dataset, encompassing multiple social media platforms and linguistic styles, was utilized to train and test the model, ensuring robustness. Results evince that the LSTM-CNN amalgamation can adeptly handle varied sentence structures and contextual nuances, outstripping traditional machine learning classifiers in both specificity and sensitivity. This research underscores the potential of hybrid neural networks in addressing contemporary digital challenges, urging further exploration into blended architectures for nuanced problem-solving in cyber realms.

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Section: Discussionmentioning

confidence: 99%

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Baiganova,

Toxanova,

Yerekesheva

et al. 2024

ijacsa

View full text Add to dashboard Cite

show abstract

“…They employed various supervised learning algorithms, including logistic regression, gradient boosting, and deep neural networks, achieving promising results. Similarly, Djuric et al (2015) explored a feature-based approach using n-grams and syntactic patterns for identifying offensive language in social media texts [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Offensive Language Identification in Low Resource Languages using Bidirectional Long-Short-Term Memory Network

Toktarova,

Abushakhma,

Adylbekova

et al. 2023

IJACSA

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Offensive language identification is a critical task in today's digital era, enabling the development of effective content moderation systems. However, it poses unique challenges in low resource languages where limited annotated data is available. This research paper focuses on addressing the problem of offensive language identification specifically in the context of a low resource language, namely the Kazakh language. To tackle this challenge, we propose a novel approach based on Bidirectional Long-Short-Term Memory (BiLSTM) networks, which have demonstrated strong performance in natural language processing tasks. By leveraging the bidirectional nature of the BiLSTM architecture, we capture both contextual dependencies and long-term dependencies in the input text, enabling more accurate offensive language identification. Our approach further utilizes transfer learning techniques to mitigate the scarcity of annotated data in the low resource setting. Through extensive experiments on a Kazakh offensive language dataset, we demonstrate the effectiveness of our proposed approach, achieving state-of-the-art results in offensive language identification in the low resource Kazakh language. Moreover, we analyze the impact of different model configurations and training strategies on the performance of our approach. The findings from our study provide valuable insights into offensive language identification techniques in low resource languages and pave the way for more robust content moderation systems tailored to specific linguistic contexts.

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“…Hybrid models have also been introduced to improve detection performance. Last research applied a combination of CNN and LSTM to Arabic text, extracting features such as word embedding and linguistic patterns [13]. Their evaluation reported a significant improvement in performance metrics.…”

Section: Related Workmentioning

confidence: 99%

Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

Sultan,

Mendes,

Kassenkhan

et al. 2023

IJACSA

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In the face of escalating cyberbullying and its associated online activities, devising effective mechanisms for its detection remains a critical challenge. This study proposes an innovative approach, integrating Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN), for the detection of cyberbullying in online textual content. The method uses LSTM to understand the temporal aspects and sequential dependencies of text, while CNN is employed to automatically and adaptively learn spatial hierarchies of features. We introduce a hybrid LSTM-CNN model which has been designed to optimize the detection of potential cyberbullying signals within large quantities of online text, through the application of advanced natural language processing (NLP) techniques. The paper reports the results from rigorous testing of this model across an extensive dataset drawn from multiple online platforms, indicative of the current digital landscape. Comparisons were made with prevailing methods for cyberbullying detection, demonstrating a substantial improvement in accuracy, precision, recall and F1-score. This research constitutes a significant step forward in developing robust tools for detecting online cyberbullying, thereby enabling proactive interventions and informed policy development. The effectiveness of the LSTM-CNN hybrid model underscores the transformative potential of leveraging artificial intelligence for social safety and cohesion in an increasingly digitized society. The potential applications and limitations of this model, alongside avenues for future research, are discussed.

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Age Group Prediction on Textual Data using Sentiment Analysis

Cited by 4 publications

References 11 publications

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Offensive Language Identification in Low Resource Languages using Bidirectional Long-Short-Term Memory Network

Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

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