Exploratory Data Analysis for Next Word Prediction

1. Introduction

This project explores text data from blogs, news, and Twitter.
The goal is to build a next-word prediction model.

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2. Load Data (SAFE VERSION - NO CRASH)

set.seed(123)

read_sample <- function(file, n = 10000) {
  con <- file(file, "r")
  lines <- readLines(con, n = n, encoding = "UTF-8", skipNul = TRUE)
  close(con)
  return(lines)
}

blogs <- read_sample("en_US.blogs.txt", 10000)
news <- read_sample("en_US.news.txt", 10000)
twitter <- read_sample("en_US.twitter.txt", 10000)

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3. Basic Summary

summary_table <- data.frame(
  Dataset = c("Blogs", "News", "Twitter"),
  Lines = c(length(blogs), length(news), length(twitter)),
  Words = c(
    sum(stri_count_words(blogs)),
    sum(stri_count_words(news)),
    sum(stri_count_words(twitter))
  ),
  AvgWords = c(
    mean(stri_count_words(blogs)),
    mean(stri_count_words(news)),
    mean(stri_count_words(twitter))
  )
)

knitr::kable(summary_table)

Dataset	Lines	Words	AvgWords
Blogs	10000	413215	41.3215
News	10000	349062	34.9062
Twitter	10000	126736	12.6736

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4. Word Distribution Plots

Blogs

blogs_wc <- stri_count_words(blogs)

ggplot(data.frame(x = blogs_wc), aes(x)) +
  geom_histogram(binwidth = 10, fill = "steelblue") +
  labs(title = "Blogs Word Distribution", x = "Words", y = "Frequency")

News

news_wc <- stri_count_words(news)

ggplot(data.frame(x = news_wc), aes(x)) +
  geom_histogram(binwidth = 10, fill = "darkgreen") +
  labs(title = "News Word Distribution", x = "Words", y = "Frequency")

Twitter

twitter_wc <- stri_count_words(twitter)

ggplot(data.frame(x = twitter_wc), aes(x)) +
  geom_histogram(binwidth = 5, fill = "tomato") +
  labs(title = "Twitter Word Distribution", x = "Words", y = "Frequency")

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5. Key Observations

• Blogs have longer text entries
  
• News is formal and structured
  
• Twitter is short and informal
  
• Data is large but manageable using sampling
  
• Suitable for building predictive text models

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6. Next Word Prediction Plan

We will build an N-gram model:

• Clean text (lowercase, remove punctuation)
• Tokenize words
• Build unigram, bigram, trigram models
• Use backoff method for prediction

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7. Shiny App Plan

The Shiny app will:

• Take user input text
• Predict next word instantly
• Use precomputed n-gram tables
• Provide a simple interface

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8. Conclusion

This analysis prepares the dataset for building a predictive text application.