Machine Learning based Spam Comments Detection on YouTube

Valpadasu, Hema; Chakri, Padala; P, Harshitha; P.M.Tarun,

doi:10.1109/iciccs56967.2023.10142608

Cited by 2 publications

(2 citation statements)

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“…H. Ohet al [14] Using a Cascaded Ensemble Machine Learning Model, the author presented a method for identifying the increasingly common YouTube spam comments. It reviewed previous research on YouTube spam comment screening and tested the efficacy of six machine learning methods (Decision tree, Logistic regression, Bernoulli Nave Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel), as well as two ensemble models (Ensemble with hard voting, Ensemble with soft voting) that combined these methods on the comment data.…”

Section: Background Studymentioning

confidence: 99%

“…Sentiment analysis, a branch of natural language processing (NLP), is critical in extracting insights from textual data by automatically assessing sentiment polarity, such as positive, negative, or neutral [10][11][12]. This research To accomplish effective sentiment prediction and categorization, modern approaches and procedures must be carefully integrated [14]. In this regard, we offer a complete method to YouTube comment prediction and sentiment classification that includes feature selection using Recursive Feature Elimination (RFE), Elastic Net Random Forest with Logistic Regression (RF with LR), and Principal Component Analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

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Youtube Comment Feature Selection And Classification Using Fused Machine Learning

R. Rooba,

2023

tjjpt

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The exponential rise of internet platforms, notably YouTube, has resulted in a massive volume of user-generated material, including video comments. Understanding audience input and enhancing user experience require analyzing and forecasting the mood of YouTube comments. This work provides a comprehensive method for YouTube comment prediction and sentiment classification that combines feature selection using Recursive Feature Elimination (RFE), Elastic Net Random Forest with Logistic Regression (RF with LR), and Principal Component Analysis (PCA). The first stage is to choose the most informative features from a given dataset using RFE, a common approach for doing so. RFE aids in the elimination of unnecessary or redundant features, resulting in enhanced model performance and decreased computing complexity. The Elastic Net Random Forest with Logistic Regression (RF with LR) technique is then used to construct a robust sentiment classification model. Elastic Net regularization combines the advantages of both L1 (Lasso) and L2 (Ridge) regularization, allowing for improved feature selection and management of multicollinearity concerns. By integrating many decision trees, the Random Forest ensemble approach improves the model's predictive potential even more. We employ Principal Component Analysis (PCA) to increase the classification model's effectiveness and solve possible difficulties created by high-dimensional data. PCA decreases the dataset's complexity while retaining its fundamental qualities, resulting in a more manageable and efficient feature space for classification. Finally, we compare the performance of three prominent classifiers on the preprocessed dataset: Linear Support Vector Machine (LSVM), Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Decision Tree (DT). We can select the best-performing model for YouTube comment categorization by comparing these classifiers.

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Section: Background Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

R. Rooba,

2023

tjjpt

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ALBERT4Spam: A Novel Approach for Spam Detection on Social Networks

Bakır,

Erbay,

Bakır

2024

Bilişim Teknolojileri Dergisi

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Engaging in social media browsing stands out as one of the most prevalent online activities. As social media increasingly integrates into our daily routines, it opens up numerous opportunities for spammers seeking to target individuals through these platforms. Given the concise and sporadic nature of messages exchanged on social networks, they fall within the realm of short text classification challenges. Effectively addressing such issues requires appropriately representing the text to enhance classifier efficiency.Accordingly, this study utilizes robust representations derived from contextualized models as a component of the feature extraction process within our deep neural network model, which is built upon the Bidirectional Long Short-Term Memory neural network (BLSTM). Introducing ALBERT4Spam, the study presents a deep learning methodology aimed at identifying spam on social networking platforms. It harnesses the proven ALBERT model to acquire contextualized word representations, thereby elevating the effectiveness of the suggested neural network framework.The random search method was used to fine-tune the ALBERT4Spam model's hyperparameters, which included the number of BLSTM layers, neuron count, layer count, activation function, weight initializer, learning rate, optimizer, and dropout, in order to obtain optimal performance. The experiments conducted on three benchmark datasets demonstrate that our innovative model surpasses widely used methods in social network spam detection. The precision results stand at 0.98, 0.96, and 0.98 for Twitter, YouTube, and SMS datasets, respectively, showcasing superior performance outcomes.

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Machine Learning based Spam Comments Detection on YouTube

Cited by 2 publications

References 6 publications

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

ALBERT4Spam: A Novel Approach for Spam Detection on Social Networks

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