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Detecting Cyberbullying Tweets Using Machine Learning and Deep Learning: An In-Depth Exploration

Jese Leos
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Published in DETECTING CYBERBULLYING TWEETS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI
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<li>**Decision Trees**: Decision trees are another popular machine learning algorithm that can be used for text classification. They create a tree-like structure that represents the decision-making process for classifying tweets. Decision trees have been shown to be effective for cyberbullying detection, providing interpretable results.</li> <li>**Naive Bayes**: Naive Bayes is a probabilistic classification algorithm that assumes conditional independence between features. It has been widely used for text classification tasks, including cyberbullying detection. Naive Bayes offers fast training time and can handle large datasets.</li>
<li>**Recurrent Neural Networks (RNNs)**: RNNs are another type of deep neural network that is particularly well-suited for processing sequential data, such as text. They can capture long-term dependencies in text and have been applied to cyberbullying detection, achieving high accuracy.</li>
<li>**Sarcasm and Irony**: Cyberbullying tweets may often contain sarcasm or irony, which can further complicate detection. Models need to be able to recognize these nuances and interpret the intended meaning of the tweet.</li> <li>**Data Imbalance**: Cyberbullying tweets are often rare compared to non-cyberbullying tweets, creating a data imbalance. This imbalance can make it difficult for models to learn effectively and may bias the detection process.</li>
<li>**Transfer Learning**: Transfer learning involves utilizing a pre-trained model from one task and adapting it to a related task. This approach can leverage the knowledge gained from a large dataset and improve the performance of cyberbullying detection models, especially when training data is limited.</li> <li>**Feature Engineering**: Feature engineering involves transforming raw data into features that are more relevant and informative for the task at hand. By extracting meaningful features from tweets, researchers can improve the effectiveness of machine learning and deep learning models for cyberbullying detection.</li>

DETECTING CYBERBULLYING TWEETS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI
DETECTING CYBERBULLYING TWEETS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI
by Vivian Siahaan

4.6 out of 5

Language : English
File size : 5678 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
Print length : 238 pages
Lending : Enabled
Hardcover : 242 pages
Item Weight : 1.14 pounds
Dimensions : 6.14 x 0.56 x 9.21 inches
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DETECTING CYBERBULLYING TWEETS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI
DETECTING CYBERBULLYING TWEETS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI
by Vivian Siahaan

4.6 out of 5

Language : English
File size : 5678 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
Print length : 238 pages
Lending : Enabled
Hardcover : 242 pages
Item Weight : 1.14 pounds
Dimensions : 6.14 x 0.56 x 9.21 inches
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