Approach : Try to use 3-4 algorithms (such as Embedding + GlobalAveragePooling1D, Embedding + Bidirectional LSTM, Embedding + Conv1D + Bidirectional LSTM) to implement the models. Perform exploratory data analysis (EDA) on the dataset before creating any models to understand its characteristics, I will try to implement 3 to 4 models for this.
abhisek247767
commented
6 hours ago
Project Title : Sarcasm Detection Model Comparison Aim : To determine the best-performing machine learning model for sarcasm detection in headlines by comparing multiple algorithms based on accuracy scores. Dataset : https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection
Approach : Try to use 3-4 algorithms (such as Embedding + GlobalAveragePooling1D, Embedding + Bidirectional LSTM, Embedding + Conv1D + Bidirectional LSTM) to implement the models. Perform exploratory data analysis (EDA) on the dataset before creating any models to understand its characteristics, I will try to implement 3 to 4 models for this.
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