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.

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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

1. 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.

2. 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.

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Objectives

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

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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.

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References

https://thecleverprogrammer.com/2021/08/24/sarcasm-detection-with-machine-learning/

🤖 Scikit-learn Documentation

🐼 Pandas Documentation

🌊 Seaborn Documentation

🖼️ Matplotlib Documentation