sarcastic-speech-detection

This project focuses on using Natural Language Processing (NLP) techniques to predict whether a headline from a news website contains sarcasm. The task involves building a predictive model using two distinct approaches for data modeling.

Installing requirements

In order to be able to run the Jupyter notebooks to enhance the code reproducibility, it is necessary to install the requirements.

pip install -r requirements.txt

Data Modeling Approaches

Word Frequency and Word Count Analysis
- Utilize the difference in word frequency between the target classes.
- Incorporate the word count of each headline as a feature for prediction.
Neural Network with Word Embeddings
- Employ a word embedding method with Keras to extract patterns in the data.
- Leverage neural network architectures to capture semantic relationships between words.

Objectives

Develop models capable of accurately identifying sarcasm in headlines.
Compare and evaluate the performance of traditional feature engineering techniques against deep learning methods.

Conclusion

This project highlights the versatility of NLP techniques by contrasting traditional and deep learning-based approaches for sarcasm detection. The insights gained from this work can be applied to broader sentiment analysis and language understanding tasks.