Natural Language Processing in the Wild.pptx
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This document discusses common NLP problems, including sentiment analysis, chatbots, translation services, and document summarization. It then presents four case studies applying NLP techniques: 1) Using chatbot conversation data and topology to understand customer groups, 2) Using text classification for product types, 3) Using topic modeling on poetry to classify poems by genre, 4) Using linguistic analysis and changepoint detection on public statements to understand changes in a leader's behavior during war. Finally, it lists helpful Python packages for NLP, topology, and modeling.
Natural Language Processing in the Wild.pptx
- 1. NLP IN THE WILD COLLEEN M. FARRELLY, DATASEMBLY
- 2. COMMON INDUSTRY NLP PROBLEMS • Sentiment analysis/tracking of customer feedback • Computational linguistics/psychology of language usage • Chatbots • Translation services • Supervised learning • Document summary
- 4. CASE STUDY 1: CONSUMER GROUP CLUSTERING • Want to understand how different groups interact with a chatbot • Sales implications • Groups-specific needs for future feature builds • Chatbot conversation data sample • NLP to derive salient text features • Persistent homology to
- 5. CASE 2: SUPERVISED LEARNING • Want to classify products by type (such as fruit or canned soup) using title text • Data includes a small sample of scraped titles from a sample of retailers with manual annotation of product type • Text cleaning and embedding algorithms to prepare the text data for machine learning • Supervised learning algorithm to create the classier
- 6. CASE 3: TOPIC MODELING • Want to find main topics discussed in a corpus of documents (poems) • Poetry data sample across genres of poetry by a single author • Topic modeling to classify poems
- 7. CASE 4: TIME- BASED ANALYSIS OF MINDSET • Want to quickly understand changes in leader’s behavior at onset of war • Public statement sample by president over course of several weeks as input data • NLP to derive linguistic features • Longitudinal models and topology- based changepoint algorithm on linguistic feature time series
- 8. HELPFUL PYTHON PACKAGES • NLP: • NLTK (parts of speech tagging, munging data…) • Gensim (topic models) • Vader (sentiment analysis) • TDA • Persim/ripser (persistent homology) • Kmapper (Mapper algorithm) • Structural equation modeling/latent class modeling • Semopy (similar to lavaan in R)
- 9. CONTACT ME • [email protected] • LinkedIn (Colleen M. Farrelly)