Text and Data Mining Guide: Module 3: Basic Text Analysis

Module 3: Basic Text Analysis

Now that your text data is cleaned and organized, it's time to dive into basic analytical methods. This module introduces core techniques for uncovering patterns and insights in your text data.

1. Word Frequency & Distribution

   • Identify the most common words in your corpus
   • Visualize distributions using methods like bar charts or word clouds

2. N-grams

   • N-grams are sequences of n words, such as bigrams (2-word sequences) or trigrams (3-word sequences)
   • Helps uncover common phrases and context not captured by single-word frequency

3. Sentiment Analysis

   • Rule-based (Lexicon): Uses dictionaries of positive/negative words (e.g., “happy” vs. “sad”)
   • Machine Learning: Trains a model on labeled data to classify sentiment (positive, negative, neutral)

Short Exercise

• Use a small text collection (e.g., product reviews or news headlines). Identify the top 5 most frequent words, any frequent bigrams, and do a quick sentiment analysis if possible.

After getting a feel for these basic analytics, you can explore more advanced methods in Module 4!

Fill out this Google Colab Notebook to practice the techniques in this module.