Natural Language Processing in Python

The speaker, Alice Chou, a senior data scientist at Metis, introduces the topic of natural language processing (NLP) in Python. She explains that the tutorial will cover an end-to-end project on NLP, including data cleaning, exploratory analysis, and applying algorithms. The audience is prepared for a two-hour session with Jupiter notebooks, and instructions for setup are provided, including downloading Anaconda and accessing GitHub for resources. The importance of understanding the basics of Python and data science packages is emphasized.

Alice Chou discusses the role of data science in NLP, highlighting the importance of having a question to guide the analysis. She explains the data science workflow, starting with a question, gathering data, performing exploratory data analysis (EDA), applying NLP techniques, and sharing insights. The speaker provides examples of NLP applications such as sentiment analysis, topic modeling, and text generation, and outlines the schedule for the day's tutorial, which includes an introduction to NLP and data science, a deep dive into Jupiter notebooks for the tutorial, and a conclusion.

The speaker introduces various Python libraries used in data analysis and NLP, such as pandas, regular expressions, scikit-learn, NLTK, TextBlob, and Gensim. She explains the process of gathering data, cleaning it, and organizing it into a standard format like a corpus or a document-term matrix. The importance of data cleaning techniques is emphasized, including removing punctuation, making text lowercase, and handling numbers and words with numbers in them. The speaker also discusses the use of pickling for saving objects for later use in Python.

Alice Chou explains the concept of EDA in the context of NLP, emphasizing its role in understanding and summarizing the main characteristics of data. She outlines the steps of EDA, which include cleaning the data, aggregating it, visualizing it, and assessing if the data makes sense. The speaker provides examples of visualizations such as word clouds and discusses how to interpret them, including the removal of common words to improve the relevance of the analysis. The goal of EDA is to ensure the data is reliable for further analysis and to identify initial trends or findings.

The speaker presents an example of analyzing comedians' routines using NLP techniques. She discusses the process of gathering transcripts of comedy routines, cleaning the text data, and creating a document-term matrix for analysis. The speaker demonstrates how to perform EDA by finding top words, assessing vocabulary size, and analyzing the amount of profanity. The goal is to understand the differences between comedians and to answer the question of what makes Ali Wong's comedy routine stand out. The speaker also discusses the importance of domain expertise in scoping the project and interpreting the results.

Alice Chou delves into sentiment analysis and topic modeling as part of the NLP tutorial. She explains sentiment analysis using the TextBlob library, which assigns polarity and subjectivity scores to text. The speaker also covers topic modeling with the Latent Dirichlet Allocation (LDA) method from the Gensim library. She provides a detailed explanation of how LDA works, including the process of assigning topics to words and refining these assignments through iterations. The goal is to identify hidden topics within the喜剧 routines and understand the distribution of these topics across different comedians' performances.

The speaker briefly touches on text generation using Markov chains as a fun exercise for creating new comedy routines based on the input corpus. She mentions that this is a simple model and that more advanced techniques like deep learning and long short-term memory (LSTM) networks can be used for more complex text generation. The speaker encourages the audience to explore the text generation notebook and apply the analysis to other comedians or text data. She also suggests looking into other NLP libraries and techniques for further study.

Alice Chou concludes the tutorial by summarizing the key points covered, including the data science workflow, NLP techniques, and the analysis of comedians' routines. She acknowledges her company, Metis, for providing the opportunity to create the tutorial materials. The speaker encourages the audience to try text generation, apply the analysis to other data, and explore further resources in data science and NLP.