Turning Text Into Insights: An Introduction to Topic Models
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Topic models are a family of unsupervised learning algorithms that can help translate raw, unlabeled text into actionable insights. This presentation provides an overview of topic models before discussing concrete examples.
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Turning Text Into Insights: An Introduction to Topic Models
- 1. AN INTRODUCTION TO TOPIC MODELING Turning text into insight:
- 2. Handling Raw, Unlabeled Text § Common Datasets: ª Product/ Customer Reviews ª Call Center Transcripts ª News Paper Articles ª Legal Documents § Common Tasks: ª Find documents were interested in? ª Categorize documents? ª Retrieve information? 2
- 3. Handling Raw, Unlabeled Text 3 § Common Datasets: ª Product/ Customer Reviews ª Call Center Transcripts ª News Paper Articles ª Legal Documents § Common Tasks: ª Find documents were interested in? ª Categorize documents? ª Retrieve information? § The Challenge ª Normal quantitative approaches don’t work with text. ª Datasets are large, complicated, sparse, and unwieldy. ª Data is often unlabeled.
- 4. Example: Understanding Customer Reviews 4 § Mon Ami Gabi is a restaurant in the Paris Paris Hotel and Casino. § Thousands of customer reviews for the restaurant over the last 8 years. What are customers saying? Excellent breakfast menu. They just need to hire more staff to have a better service. Great place for brunch! Highly recommend the steak and fries and sitting outside. Had a great meal with a great atmosphere Food was ok… What it has going for it is the view from the outside terrace.
- 5. Topic Modeling: Framework 5 Excellent breakfast menu. They just need to hire more staff to have a better service Breakfast Quality of Service breakfast better service staff Documents Topics Words and Phrases
- 6. Topic Modeling: Preprocessing 6 § Tokenize: Extract meaningful units from sentences ª I ordered a french toast ª Regular expression cleanup, end-‐of-‐line hyphenation, contraction, and sentence-‐initial capitalization rules. § Stemming Algorithm: Consolidate feature space into word stems or lemmas ª {I, ordered, a, french toast} ª Suffix stripping, part of speech tagging § Matrix Factorization: Convert text into data structure for learning algorithms. ª Word-‐document matrices often have 1,000,000,000,000+ values. Need special compression algorithms to make data manageable. {I, ordered, a, french toast} {I, order, a, french toast}
- 7. Topic Modeling: Estimation with Gibbs Sampler 7 ª Use Markov Chain Monte Carlo methods to simulate our document-‐topic and topic-‐ word probability distributions. ª Results: Topic-‐Word Breakfast Service Breakfast: 0.31 Service: 0.28 Eggs: 0.27 Staff: 0.24 Coffee: 0.24 Friendly: 0.21 Document-‐Topic The french toast was great The staff was great, but the outdoor patio was a bit noisy. French Toast: 0.71 Service: 0.51 Breakfast: 0.25 Environment: 0.44 Service: 0.03 Breakfast: 0.02
- 8. Harnessing the Model: Topic Frequency 8 What are my customers talking about?
- 9. Harnessing the Model: Evaluate Products and Verticals 9 How do customers feel about my products?
- 10. Harnessing the Model: Temporal Insights 10 How has customer sentiment evolved among my product lines over time?
- 11. Harnessing the Model: Deep Product Insights 11 Which properties of French Toast drive satisfaction (or dissatisfaction)?
- 12. Thank you.