# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 3 Proposed Work
### Classification Using Advanced Models  
The classification process began with the application of simple machine-learning (ML) models and progressively moved toward more sophisticated deep-learning (DL) and Transformer-based architectures to improve predictive accuracy and handle the complexities of online misogyny detection. Initially, we implemented traditional ML algorithms, including Decision Tree (DT), Support Vector Machines (SVM), Random Forests (RF), and Logistic Regression (LR), Naive Bayes (NB), Artificial Neural Network (ANN). These models served as baseline classifiers and demonstrated the importance of feature engineering in capturing relevant patterns. While effective for simpler tasks, these models were limited in their ability to handle complex relationships and contextual dependencies inherent in the text. To address these limitations, we advanced to deep-learning models, which are better equipped to capture spatial and temporal dependencies in the data. We employed Convolutional Neural Networks (CNNs) to recognize local patterns and Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Units (GRU) to capture sequential relationships within the text. These architectures allowed for more sophisticated handling of text data, especially for understanding context and word order. Building on the strengths of deep learning, we then integrated Transformer-based models such as BERT (Bidirectional Encoder Representations from Transformers), RoBERTa, DistilBERT, XLNET, Albert, and Electra. These models are designed to understand complex contextual relationships by processing the entire sequence of text simultaneously rather than sequentially. Their attention mechanisms enable the models to weigh the importance of different words in a given context, making them highly effective for capturing nuanced linguistic patterns in misogynistic content. Fine-tuning these Transformers on the specific task further enhanced their performance, leading to substantial improvements in classification accuracy. A **Convolutional Neural Network (CNN)** is designed to process data with a grid-like structure, making it especially effective for image data [35]. CNNs apply convolution operations that extract spatial hierarchies from the input data by convolving a filter \(K\) with an input \(I\), defined mathematically as:  
\[(I*K)(i,j)=\sum_{m}\sum_{n}I(m,n)\cdot K(i-m,j-n)\]  
where \((i,j)\) denotes the position on the output feature map. The CNN's architecture enables it to capture local dependencies and spatial hierarchies. The **Long Short-Term Memory (LSTM)** network is a type of recurrent neural network that overcomes the problem of the disappearance of the gradient using cell states and gating mechanisms [36]. This allows the model to retain important long-term information across sequences. An LSTM cell is mathematically defined as follows:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}),\quad i_{t}=\sigma(W_{i}\cdot[h_ {t-1},x_{t}]+b_{i}),\]  
\[\tilde{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}),\quad C_{t}=f_{t}*C_{t-1} +i_{t}*\tilde{C}_{t},\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}),\quad h_{t}=o_{t}*\tanh(C_{t})\]  
where \(f_{t}\), \(i_{t}\), and \(o_{t}\) represent the forget, input, and output gates, respectively. This gating mechanism enhances prediction accuracy in sequence-based tasks, such as natural-language processing (NLP). The **Bidirectional LSTM (BiLSTM)** extends LSTM by incorporating both forward and backward passes, enabling it to capture dependencies from past and future contexts [37].