This project was part of my Natural Language Processing course and explores the application of Natural Language Processing (NLP) techniques for text analysis. The primary goal is to understand and classify the political bias of responses generated by the ChatGPT language model. The project involves various steps, including data preprocessing, classification, and consideration of the limitations associated with such analyses.
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Data Collection and Preprocessing: The project started with the collection of responses from ChatGPT. These responses were then preprocessed to prepare them for classification, including tasks such as cleaning, tokenization, and encoding.
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Classification Model: A classification model, based on techniques such as Naive Bayes, Random Forest, or Support Vector Machines, was applied to categorize responses into political bias categories, such as left, right, or center.
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Limitations and Considerations: A critical aspect of the project involved acknowledging and addressing the limitations inherent in classifying political bias. This includes challenges related to subjective labeling, lack of labeled data, and the nuanced nature of political language.
The project yielded insights into the complexities of classifying political bias in text, highlighting challenges in interpretation, subjectivity, and the influence of contextual nuances. The results underscored the importance of considering these factors in similar text analysis projects.
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Data: The dataset used or generated during this project may be made available for further analysis.
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Code: Relevant code snippets, scripts, or notebooks can be found in this repository.
While this project provides a foundational understanding of political bias classification, future work could focus on enhancing model accuracy, addressing biases, and exploring advanced techniques in NLP.
- Python
- NLP-specific packages