Introduction
This project involved performing topic modeling on a document set using Latent Dirichlet Allocation (LDA) to uncover hidden themes and visualize the results. For completing the task, I chose the same theme as my previous task – corruption. Did research of articles and essays about corruption in Europe.
Methodology
- Data Preparation:
- Documents were stored in a CSV file (articles.csv) with columns id and content.
- Text was cleaned by removing HTML tags and extra whitespace.
- NLTK was used for tokenization, lemmatization, and removing stopwords provided in stopwords_en.txt.
- Text was converted into a document-term matrix using CountVectorizer.
- The LDA model was built using LatentDirichletAllocation with specified parameters.
- Word clouds were generated for each topic.
- Topic distribution across documents was plotted.
Results
- Topic Interpretation: Each topic was represented by high-probability words, e.g., a financial misconduct topic included words like "fraud," "bribery," and "corruption."
- Topic Distribution: The prevalence of topics varied across documents, providing insights into the thematic structure.
- Relevance: Topics were meaningful and aligned with the document context.
Conclusion
LDA effectively revealed themes in the text corpus. Preprocessing enhanced topic relevance, and visualizations provided clear insights. Future work could involve fine-tuning the model and exploring other techniques.
Summary:
- Data Preparation: Cleaned and preprocessed text.
- Vectorization: Created document-term matrix.
- LDA Model: Identified key words and topics.
- Visualization: Generated word clouds and topic distributions.
- Results: Meaningful topics and varied distributions.
- Conclusion: Effective method; future improvements planned.
This approach provides a robust foundation for understanding text data themes using LDA.
As a results of visualization:
