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CoP: Data Science: Create Text Analysis Tutorial #153

Closed as not planned
Closed as not planned

Description

@akhaleghi

Overview

We need to create a text analysis tutorial, so that new members can get familiar with the concepts and have a roadmap for what to do, if we ask for it.

Action Items

  • Create a Google Doc in the folder provided under resources
    • Draft an introductory paragraph explaining what the tutorial resources cover and why a new data scientist would use them for working with data at Hack For LA
    • Identify resources with vetted tutorials covering important skills within the tutorial area, adding to the draft
  • Review the draft with the Data Science CoP
  • Add to the wiki page

Resources/Instructions

Wiki page

Location for any files you might need to upload (drafts, images, etc.)

Tools that are core that should be mentioned:

  • nltk
  • SpaCy

Examples of resources that would be useful to include:

  • Web how-to/tutorial/walk-throughs
  • Youtube playlists or videos demonstrating tools
  • Links to blogs or platforms with subject matter experts

Activity

  1. bfang22 commented on Apr 23, 2024

    @bfang22
    Member

    Hi - I might be able to work on this. I brainstormed an outline of topics, but would appreciate some feedback on scope and where to draw the line. Should this be more focused on text processing/analysis fundamentals or do we want to go all the way to building and evaluating predictive/classification models?

    Potential tutorials:

    Use Cases

    • Most frequent tokens, entities (beginner)
    • Prediction/Classification (intermediate)
      • Sentiment, Next Word, other user-defined outcomes
    • Language Inference, Translation (advanced)

    Text Analysis Basics

    Pre-processing

    • Stop Words
    • Lemmatization vs Stemming
    • Tokenization
    • N-grams

    Analysis basics (with libraries)

    • Named Entity Recognition
    • Part of Speech Tagging
    • Topic Modeling

    More advanced...

    Vectorization/Encoding

    • One-hot encoding (Beginner)
    • BOW (Beginner)
    • TF-IDF (Intermediate)
    • Word embeddings (advanced)

    Building Models

    • Logistic Regression
    • MLP
    • SVM

    Hyperparameter Tuning

    • Bias/variance tradeoff
    • Learning rate, epochs, etc

    Metrics

    • Accuracy, Precision, Recall, and F1
  2. bfang22 commented on Apr 23, 2024

    @bfang22
    Member

    Feedback from DS meeting:

    • Connect topics and tutorials to HFLA datasets and projects

    • Build tutorial/notebook using HFLA dataset

    • Review 311 dataset to see if there's textual data that's a good fit

    • Explore alternatives: affordable housing or scraping HFLA agenda issues

  3. moved this to In progress (actively working) in CoP: Data Science: Project Boardon Jun 10, 2024
  4. bfang22 commented on Jun 11, 2024

    @bfang22
    Member
    • Identified 2 datasets with textual data: Los Angeles County Department of Arts and Culture's data on Community Impact Art Grants and Organizational Grants Program
    • Create tutorial (jupyter notebook)
      • pre-processing
        • using nltk library (stopwords, tokenizers, stem)
  5. moved this from In progress (actively working) to Done in CoP: Data Science: Project Boardon Jun 18, 2024
  6. 2 remaining items

  7. changed the title [-]Create Text Analysis Tutorial[/-] [+]CoP: Data Science: Create Text Analysis Tutorial[/+] on Jun 18, 2024
  8. akhaleghi commented on May 19, 2025

    @akhaleghi
    Author
  9. moved this from In progress (actively working) to Prioritized Backlog in CoP: Data Science: Project Boardon May 19, 2025
  10. moved this from Prioritized Backlog to New Issue Approval in CoP: Data Science: Project Boardon Sep 9, 2025
  11. chinaexpert1 commented on Sep 9, 2025

    @chinaexpert1
    Member

    Discussed this with Bonnie. These are all the possible tasks in Text Analysis: NLP at Medium

    We are creating an Epic as a parent to all these classic NLP tasks that will become a genesis of tutorials on traditional NLP techniques, and how they are now solved by LLMs such as ChatGPT in many instances. I need to do that before Monday.

  12. self-assigned this
    on Sep 9, 2025
  13. moved this from New Issue Approval to In progress (actively working) in CoP: Data Science: Project Boardon Sep 9, 2025
  14. chinaexpert1 commented on Sep 13, 2025

    @chinaexpert1
    Member

    Duplicate of #227

    Instructions
    1. Progress: "What is the current status of your project? What have you completed and what is left to do?"
    2. Blockers: "Difficulties or errors encountered."
    3. Availability: "How much time will you have this week to work on this issue?"
    4. ETA: "When do you expect this issue to be completed?"
    5. Pictures (if necessary): "Add any pictures that will help illustrate what you are working on."
    1. Progress: Made the Epic. Next is to make the sub-issues but I want to gauge CoP interest before moving forward. If no one is interested in learning classical NLP, then its not urgent.
    2. Blockers: Want CoP input
    3. Availability: Monday
    4. ETA: 1 week
    5. Pictures (if necessary):
  15. moved this from In progress (actively working) to Ice Box in CoP: Data Science: Project Boardon Sep 20, 2025
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