Executive Summary

This report presents an exploratory data analysis of a text corpus consisting of blog posts, news articles, and Twitter messages. The goal is to understand the structure and patterns in the data as a foundation for building a predictive text model (similar to SwiftKey’s keyboard). Key findings include:

  • Dataset size: [X] total lines across three sources
  • Vocabulary: [X] unique words identified
  • Coverage: Just [X] words account for 50% of all word instances
  • Next steps: Build n-gram model for word prediction

1. Introduction

1.1 Project Background

Mobile typing is challenging, and predictive text models help users type faster and more accurately. This project aims to build a predictive text model using natural language processing (NLP) techniques, specifically n-gram modeling.

1.2 Dataset Overview

The dataset consists of text from three sources:

  • Blogs: Personal blog posts
  • News: News articles
  • Twitter: Social media messages (tweets)

These sources represent different writing styles and contexts, providing a diverse corpus for training the predictive model.

1.3 Objectives

  1. Perform exploratory data analysis on the text corpus
  2. Analyze word frequency patterns (unigrams, bigrams, trigrams)
  3. Determine vocabulary coverage requirements
  4. Identify challenges and plan next steps for model building

2. Data Loading and Preprocessing

2.1 Sampling Strategy

Due to the large size of the original files, I created a representative sample using random sampling:

# Example sampling code (adjust to show your actual approach)
set.seed(123)
sample_rate <- 0.05  # 5% sample

# Sample from each file
blogs_sample <- sample_file("en_US.blogs.txt", sample_rate)
news_sample <- sample_file("en_US.news.txt", sample_rate)
twitter_sample <- sample_file("en_US.twitter.txt", sample_rate)

Justification: A 5% random sample provides sufficient data for exploratory analysis while maintaining computational efficiency. Random sampling ensures the sample is representative of the full dataset.

2.2 Data Cleaning

The following text preprocessing steps were applied:

  1. Lowercase conversion: Standardize all text to lowercase
  2. Number removal: Remove numeric values (not relevant for word prediction)
  3. Punctuation removal: Strip punctuation marks
  4. Whitespace normalization: Remove extra spaces

Important: Stopwords (common words like “the”, “is”, “at”) were NOT removed because they are essential for natural language prediction.

# Text cleaning with tm package
corpus <- VCorpus(VectorSource(corpus_text))
corpus_clean <- tm_map(corpus, content_transformer(tolower))
corpus_clean <- tm_map(corpus_clean, removeNumbers)
corpus_clean <- tm_map(corpus_clean, removePunctuation)
corpus_clean <- tm_map(corpus_clean, stripWhitespace)

2.3 Dataset Summary

Dataset Summary Statistics
Source Lines Words
Blogs XX,XXX XXX,XXX
News XX,XXX XXX,XXX
Twitter XX,XXX XXX,XXX
Total XX,XXX X,XXX,XXX

3. Exploratory Analysis

3.1 Unigram Analysis (Single Words)

Unigrams are individual words. Analyzing their frequency helps us understand which words are most common in the corpus.

Top 20 Most Frequent Words

Top 20 Most Frequent Unigrams

Top 20 Most Frequent Unigrams

Key Observations

  • Stopwords dominate: As expected, common words like “the”, “to”, “and” are most frequent
  • These words provide grammatical structure but less semantic meaning
  • Content words (nouns, verbs) appear lower in frequency but carry more meaning
  • Total unique unigrams: [INSERT NUMBER]

3.2 Bigram Analysis (Two-Word Phrases)

Bigrams capture common two-word sequences, revealing patterns in how words are combined.

Top 20 Most Frequent Bigrams

Top 20 Most Frequent Bigrams

Top 20 Most Frequent Bigrams

Key Observations

  • Common phrases like “of the”, “in the”, “to the” dominate
  • Some bigrams show context-specific patterns (e.g., “last year”, “new york”)
  • Bigrams provide more context than unigrams for prediction
  • Total unique bigrams: [INSERT NUMBER]

3.3 Trigram Analysis (Three-Word Phrases)

Trigrams capture longer phrases and more specific contexts.

Top 20 Most Frequent Trigrams

Top 20 Most Frequent Trigrams

Top 20 Most Frequent Trigrams

Key Observations

  • Trigrams are more specific and context-dependent
  • Common complete phrases emerge (e.g., “one of the”, “a lot of”)
  • Much larger vocabulary space with lower individual frequencies
  • Total unique trigrams: [INSERT NUMBER]

3.4 Word Cloud Visualization

A word cloud provides a visual summary of the most frequent terms, with size indicating frequency.

Word Cloud of Most Frequent Terms

Word Cloud of Most Frequent Terms

4. Coverage Analysis

4.1 Vocabulary Coverage

A key question for model building: How many unique words do we need to cover most text?

This is important for: - Memory efficiency - Model size - Prediction accuracy vs. computational cost

Cumulative Word Coverage Curve

Cumulative Word Coverage Curve

4.2 Coverage Statistics

Words Needed to Cover Text Instances
Coverage Words_Needed Percentage_of_Vocabulary
50% [INSERT] [INSERT]%
90% [INSERT] [INSERT]%

Key Insights

  • 50% Coverage: Only [X] unique words account for half of all word instances
  • 90% Coverage: [X] words needed to cover 90% of instances
  • This follows Zipf’s Law: A small number of words occur very frequently, while most words are rare
  • Implication: Can build an effective model with a manageable vocabulary size

5. Data Quality and Challenges

5.1 Observations

Based on the exploratory analysis, several challenges were identified:

  1. Profanity: The dataset contains offensive words (intentionally left for handling)
  2. Misspellings: Social media text contains typos and informal language
  3. Foreign words: Some non-English words present despite filtering
  4. Rare words: Long tail of words that appear only once or twice
  5. Context sensitivity: Same words have different meanings in different contexts

5.2 Considerations for Model Building

  • Out-of-vocabulary words: How to handle words not seen during training?
  • Smoothing: Techniques needed to assign probabilities to unseen n-grams
  • Memory constraints: Cannot store all possible n-grams
  • Speed: Real-time prediction requires fast lookup
  • Accuracy vs. size tradeoff: Larger models may be more accurate but slower

6. Next Steps and Modeling Plan

6.1 Planned Approach

The predictive text model will use an n-gram language model with the following components:

6.1.1 N-gram Model Architecture

  1. Build n-gram frequency tables (already completed in this analysis)
  2. Calculate conditional probabilities: P(word | previous words)
  3. Implement backoff strategy:
    • Try trigram first
    • If not found, back off to bigram
    • If not found, back off to unigram
    • Use uniform distribution for unknown words

6.1.2 Smoothing Techniques

Consider implementing: - Katz Backoff: Discount probability mass for seen n-grams, redistribute to unseen - Stupid Backoff: Simplified approach suitable for large datasets - Good-Turing Smoothing: Adjust frequencies based on frequency of frequencies

6.1.3 Optimization Strategies

To make the model efficient: - Pruning: Remove very low-frequency n-grams - Hashing: Use hash tables for fast lookup - Compression: Store only necessary information - Top-K prediction: Return only top 3-5 predictions

6.2 Evaluation Plan

Model performance will be evaluated using: - Perplexity: How well the model predicts held-out test data - Accuracy: Percentage of correct top-1, top-3 predictions - Speed: Response time for predictions - Coverage: Percentage of test queries the model can handle

6.3 Shiny App Requirements

The final application will: 1. Accept text input (multiple words) 2. Predict next word(s) using the trained model 3. Display top 3 predictions 4. Provide fast, real-time response 5. Handle edge cases gracefully

7. Conclusion

This exploratory analysis has provided valuable insights into the text corpus structure:

  • Large vocabulary with most words appearing infrequently (Zipf’s Law)
  • N-grams capture patterns at different levels of context
  • Coverage analysis shows we can build practical models with manageable vocabulary
  • Identified challenges that need to be addressed in model building

The next phase will focus on building an efficient n-gram model with appropriate smoothing and backoff strategies to create a functional predictive text application.

Appendix: Code

All code used in this analysis is available in the accompanying R scripts:

  • task1_data_sampling.R: Data loading and sampling
  • task2_exploratory_analysis.R: N-gram analysis and visualization
sessionInfo()
## R version 4.5.1 (2025-06-13 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26100)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=English_South Africa.utf8  LC_CTYPE=English_South Africa.utf8   
## [3] LC_MONETARY=English_South Africa.utf8 LC_NUMERIC=C                         
## [5] LC_TIME=English_South Africa.utf8    
## 
## time zone: Africa/Johannesburg
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.37     R6_2.6.1          fastmap_1.2.0     xfun_0.52        
##  [5] cachem_1.1.0      knitr_1.50        htmltools_0.5.8.1 png_0.1-8        
##  [9] rmarkdown_2.30    lifecycle_1.0.4   cli_3.6.5         sass_0.4.10      
## [13] jquerylib_0.1.4   compiler_4.5.1    rstudioapi_0.17.1 tools_4.5.1      
## [17] evaluate_1.0.4    bslib_0.9.0       yaml_2.3.10       rlang_1.1.6      
## [21] jsonlite_2.0.0

Note: This report was created as part of the Coursera Data Science Capstone project.