Concluding Remarks

Introduction to Text Analysis with Python

Bogdan G. Popescu
bogdan.popescu@johncabot.edu

John Cabot University

Learning Outcomes

Overview

Some of the learning outcomes in this course focused on:

  • Write Python programs to perform loops, conditional statements, and function definitions.
  • Employ quantitative techniques to process and analyze textual data.
  • Utilize Python libraries like Numpy, Pandas, and NLTK for text manipulation and analysis.
  • Apply advanced methods such as sentiment analysis, topic modeling, word embeddings, and supervised learning to text data.

Learning Outcomes

Overview

Some of the learning outcomes in this course focused on:

  • Use ChatGPT and prompt engineering to enhance text analysis tasks, including summarization, classification, and generating structured outputs.
  • Create and publish a professional website showcasing their portfolio and analytical capabilities.

Introduction

Jobs where these skills are valued

Data Science and Analytics

  • Data Scientist: Developing predictive models and conducting advanced analysis with text data.
  • Text/Language Data Analyst: Extracting insights from text data for business or research.

Introduction

Jobs where these skills are valued

Natural Language Processing (NLP) and AI

  • NLP Engineer: Working on synthesizing customer reviews, language translation, and sentiment analysis.
  • AI Prompt Engineer: Designing and optimizing prompts for large language models like ChatGPT.

Research and Academia

  • Research Scientist: Conduct text-based research in political science, sociology, or computational linguistics.
  • Academic/Teaching Positions: Teaching Python and text analysis at universities or boot camps.

Introduction

Jobs where these skills are valued

Communication and Technical Writing

  • Technical Writer: Explain complex computational methods clearly and in a structured manner.
  • Science Communicator: Building accessible content from technical analyses.

Business and Consulting

  • Business Analyst: Using Python and text analysis to drive data-driven decision-making.
  • Consultant: Advising clients on leveraging data and text-based insights.

Use of Python

Both R and Python are (more commonly) used in a variety of fields:

  • Finance
  • Academic research
  • Government
  • Retail
  • Data Journalism
  • Healthcare

Books

Text Analysis

McKinney, Wes, 2022.Python for Data Analysis

Hovy, Dirk. 2020. Text Analysis in Python for Social Scientists, Cambridge University Press.

Bengfort, Benjamin, Bilbro, Rebecca, and Tony Ojeda. 2018. Applied Text Analysis with Python. O’Reilly Media.

Books

Text Analysis

Hvitfeldt, Emil and Silge, Julia. 2022. Supervised Machine Learning for Text Analysis in R, CRC Press.

Grimmer, Justin, Brandon M. Stewart, and Margaret E. Roberts. 2022. Text As Data. Princeton University Press

Overview

Table of Contents

Week 1: Intro to Python, Jupyter notebooks, and R Quarto
Week 2: Variables, Loops, Lists, Breaks
Week 3: Lists, Tuples, While Loops
Week 4: Dictionaries, Pandas
Week 5: Pandas Data Wrangling
Week 6: Data Visualization
Week 7: Text analysis and ChatGPT Intro

Week 8: ChatGPT Summarization, Classification, Sentiment Analysis, Topic Modeling
Week 9: Text Similarity
Week 10: Language Complexity
Week 11: Topic Models and Word Embeddings
Week 12: Student Proposal
Week 13: Sentiment Analysis
Week 14: Course Reflection

Supplementary Lectures

I encourage you to check out the supplementary lectures:

Final Projects

This project offers an opportunity to showcase the acquired skills in manipulating text data and conducting meaningful analyses.

Tasks for the Project:

  • choosing a topic that involves text data
  • acquire data either from the course materials or from external sources

Students have to submit a two-page report in Quarto and give a 20-minute presentation in Quarto.

Final Projects

Grading Criteria for the project

  • Relevance
  • Methodology
  • Analysis
  • Presentation
  • Code Quality

Final Projects

Instructions

Presentations should be 15 minutes.

Rehearse at least twice at home

Focus your presentation on the story

Criteria for Presentation Grading

Content Knowledge

Organization

Clarity of Expression

Engagement with Audience

Visual Aids

Time Management

Moving Forward

Update your CV

  • Programming in Python for data and text analysis
  • Cleaning, manipulating, and analyzing text data
  • Using large language models (e.g., ChatGPT) for advanced text tasks
  • Advanced Text Analysis Methods: sentiment analysis, topic modeling, word embeddings, and supervised machine learning
  • Prompt Engineering with ChatGPT

Course Feedback and Evaluation

  1. What are some aspects of the course that you liked?
  2. What are areas for improvement?
  3. Let’s do the course evaluations.

Conclusion

Thank you and good luck!