Exploratory Analysis of Text Data for Prediction Model

Introduction

The goal of this project is to build a text prediction model using Natural Language Processing (NLP) techniques and deploy it in a Shiny web application. The data includes text from blogs, news articles, and Twitter messages.

This report demonstrates:

• Successful loading and preprocessing of the data
  
• Basic exploratory analysis
  
• Summary statistics and visualizations
  
• A plan for building the predictive model and Shiny app

---

Data Description

We are using three datasets:

blogs <- readLines(“en_US/en_US.blogs.txt”, warn = FALSE, encoding = “UTF-8”) news <- readLines(“en_US/en_US.news.txt”, warn = FALSE, encoding = “UTF-8”) twitter <- readLines(“en_US/en_US.twitter.txt”, warn = FALSE, encoding = “UTF-8”)

Each file contains a large collection of text lines.

# Load data (adjust path as needed)
blogs <- readLines("en_US.blogs.txt", warn = FALSE, encoding = "UTF-8")
news <- readLines("en_US.news.txt", warn = FALSE, encoding = "UTF-8")
twitter <- readLines("en_US.twitter.txt", warn = FALSE, encoding = "UTF-8")

---

Summary Statistics

# Basic statistics
data_summary <- data.frame(
  Source = 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))),
  MaxLineLength = c(max(nchar(blogs)),
                    max(nchar(news)),
                    max(nchar(twitter)))
)
knitr::kable(data_summary)

Source	Lines	Words	MaxLineLength
Blogs	899288	37546806	40833
News	1010206	34761151	11384
Twitter	2360148	30096649	140

---

Line Length Distribution

line_lengths <- data.frame(
  Source = rep(c("Blogs", "News", "Twitter"), times = c(length(blogs), length(news), length(twitter))),
  LineLength = c(nchar(blogs), nchar(news), nchar(twitter))
)

ggplot(line_lengths, aes(x = LineLength, fill = Source)) +
  geom_histogram(binwidth = 20, alpha = 0.6, position = "identity") +
  xlim(0, 1000) +
  labs(title = "Distribution of Line Lengths", x = "Line Length", y = "Count")

---

Word Frequency Analysis

# Sample 1% for quick analysis
set.seed(123)
sample_text <- c(sample(blogs, length(blogs) * 0.01),
                 sample(news, length(news) * 0.01),
                 sample(twitter, length(twitter) * 0.01))

sample_df <- data.frame(text = sample_text)

# Clean and tokenize
tokens <- sample_df %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words)

top_words <- tokens %>%
  count(word, sort = TRUE) %>%
  top_n(20)

ggplot(top_words, aes(x = reorder(word, n), y = n)) +
  geom_col() +
  coord_flip() +
  labs(title = "Top 20 Most Common Words", x = "Word", y = "Frequency")

---

Next Steps

We plan to:

• Further clean the data (remove profanity, punctuation, etc.)
• Build N-gram models (unigrams, bigrams, trigrams)
• Implement a backoff prediction algorithm
• Optimize performance for use in a Shiny web app
• Allow users to input partial sentences and receive predictions in real-time

---

Conclusion

The exploratory analysis shows that:

• The datasets are large and varied, especially Twitter which has many short lines
• Word frequency patterns follow expected linguistic distributions
• We are on track to build a functional prediction model and deploy it as an interactive application

Thank you for your time. Feedback is welcome!