Exploratory Analysis of Text Data for Next-Word Prediction

Author

Swarnika Shakya

Exploratory Analysis of Text Data for Next-Word Prediction

1. Overview

This project explores a large text dataset to prepare for building a next-word prediction model and an interactive Shiny application.

The purpose of this report is to:

  • Confirm successful data loading

  • Summarize key characteristics of the dataset

  • Identify patterns relevant to prediction

  • Outline the approach for the final model and application

2. Data Description

The dataset consists of three English-language sources:

  • Blogs

  • News articles

  • Twitter posts

These sources provide a mix of structured and conversational language, which is important for building a generalizable prediction system.

3. Data Loading and Summary

All datasets were successfully loaded into R and analyzed.

Basic Summary Statistics

# Example code (replace with your actual data)

library(stringi)


files <- c(“blogs.txt”, “news.txt”, “twitter.txt”)


summary_stats <- data.frame(

 File = files,

 Lines = sapply(files, function(f) length(readLines(f))),

 Words = sapply(files, function(f) sum(stri_count_words(readLines(f))))

)


summary_stats

Observations

  • Twitter contains the largest number of lines but shorter entries

  • Blogs and news contain longer, more structured text

  • The dataset contains tens of millions of words, sufficient for modeling

4. Data Sampling

Due to the large size, a small random sample (1–5%) was used for analysis.
This reduces computation time while preserving overall patterns.

5. Data Cleaning

The text data was preprocessed using:

  • Lowercasing

  • Removal of punctuation and numbers

  • Removal of stopwords

  • Whitespace normalization

This ensures consistency for analysis and modeling.

6. Exploratory Analysis

6.1 Word Frequency Distribution

library(ggplot2)


word_counts <- table(sample_words)  # replace with your processed words


df <- data.frame(freq = as.numeric(word_counts))


ggplot(df, aes(x = freq)) +

 geom_histogram(bins = 50) +

 ggtitle(“Histogram of Word Frequencies”)

Insight

  • Word usage is highly skewed

  • A small number of words appear very frequently

6.2 N-gram Analysis

# Example bigram frequency

bigrams <- table(sample_bigrams)  # replace with your bigram data


top_bigrams <- sort(bigrams, decreasing = TRUE)[1:10]

top_bigrams

Insight

  • Word sequences (bigrams/trigrams) capture meaningful context

  • These are essential for accurate prediction

7. Key Findings

  • The dataset follows a natural language frequency distribution

  • Context improves prediction significantly over single words

  • Twitter data adds conversational variability

  • Blogs and news improve grammatical structure

8. Plan for Prediction Algorithm

The model will use an N-gram approach with backoff:

  • Trigrams for primary prediction

  • Bigrams as fallback

  • Unigrams as final fallback

This ensures predictions even when exact matches are not found.

Future improvements:

  • Smoothing techniques for unseen sequences

  • Performance optimization for fast lookup

9. Plan for Shiny Application

The application will:

  • Accept user text input

  • Predict the next word in real time

  • Display results instantly

Focus areas:

  • Fast response time

  • Simple interface

  • Accessibility via browser

10. Conclusion

The dataset has been successfully loaded and analyzed.
Key linguistic patterns have been identified, and a clear approach has been defined for building both the prediction algorithm and the Shiny application.