Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models

Das, Rajesh Kumar; Islam, Mirajul; Hasan, Md Mahmudul; Razia, Sultana; Hassan, Mocksidul; Khushbu, Sharun Akter

doi:10.1016/j.heliyon.2023.e20281

Cited by 18 publications

(1 citation statement)

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“…With a knack for pattern detection, these models exhibit remarkable accuracy in discerning sentiment, a fact substantiated by various studies. The crux lies in their application to the extracted textual content, scrutinizing whether the context of a message veers into offensive territory [14][15][16]. This paper makes a distinctive contribution to the field by crafting a framework adept at extracting and classifying text from images within online messages.…”

Section: Introductionmentioning

confidence: 99%

Graph convolution networks for social media trolls detection use deep feature extraction

Asif,

Al-Razgan,

Ali

et al. 2024

J Cloud Comp

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This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed a machine-learning model capable of detecting toxic images through their embedded text content. Our approach leverages GloVe word embeddings to enhance the model's predictive accuracy. We also utilized Graph Convolutional Networks (GCNs) to effectively analyze the intricate relationships inherent in social media data. The practical implications of our work are significant, despite some limitations in the model's performance. While the model accurately identifies toxic content more than half of the time, it struggles with precision, correctly identifying positive instances less than 50% of the time. Additionally, its ability to detect all positive cases (recall) is limited, capturing only 40% of them. The F1-score, which is a measure of the model's balance between precision and recall, stands at around 0.4, indicating a need for further refinement to enhance its effectiveness. This research offers a promising step towards more effective monitoring and moderation of toxic content on social platforms.

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Section: Introductionmentioning

confidence: 99%