Exploratory Analysis of Text Data for a Next-Word Prediction Model

Author: Nakul Rajawat Date: January 2026

Introduction

The objective of this project is to explore and understand large text datasets that will be used to build a next-word prediction model. This milestone focuses on performing exploratory data analysis to gain familiarity with the structure, size, and characteristics of the data. The insights obtained from this analysis will support the design of an efficient prediction algorithm and a Shiny application for text input and word prediction.

Data Description

The dataset used in this project consists of three large text files obtained from different sources: blogs, news articles, and Twitter posts. These files represent real-world unstructured text data and are commonly used for natural language processing tasks. The data was successfully downloaded and prepared for analysis.

The three datasets are:

Blogs data containing long-form personal and informational writing. News data containing formal and structured articles. Twitter data containing short and informal text messages.

Each dataset differs significantly in writing style, length, and structure, making them useful for building a robust prediction model.

Summary Statistics

Basic summary statistics were considered to understand the size and scope of each dataset. These statistics include the number of lines, words, and characters in each file.

The Twitter dataset contains the highest number of lines, indicating a large volume of short text entries. In contrast, blogs and news datasets contain fewer lines but longer content per line, suggesting more detailed and structured text. The blogs dataset includes a mix of personal opinions and informational content, while the news dataset follows a more formal and consistent writing style.

These differences highlight the diversity of the text sources and emphasize the need for careful preprocessing before model building.

Exploratory Data Analysis

Exploratory analysis was conducted to better understand word usage and sentence structure within the datasets. Blog and news content generally contain longer sentences and more complex vocabulary, while Twitter text is concise and informal.

Common words such as “the”, “and”, and “to” appear frequently across all datasets, indicating the importance of removing stop words during preprocessing. The variation in sentence length and writing style suggests that normalization techniques such as lowercasing, punctuation removal, and tokenization will be essential.

The exploratory findings confirm that combining these datasets will improve the generalization of the prediction model by exposing it to multiple forms of natural language usage.

Interesting Findings

The datasets differ significantly in tone, structure, and content length. Twitter data is short and conversational, news articles are formal and structured, and blogs combine both personal and informational styles. This diversity enhances the richness of the corpus but also increases preprocessing complexity.

The presence of noise such as symbols, abbreviations, and informal language highlights the need for extensive text cleaning. Addressing these challenges will be critical for building an accurate and efficient next-word prediction system.

Plan for Prediction Algorithm and Shiny Application

The next phase of the project will focus on cleaning and preprocessing the text data. This includes converting text to lowercase, removing punctuation, numbers, stop words, and filtering profane terms.

N-gram models will be developed to capture word sequences and predict the next word based on user input. The final model will be implemented in a Shiny web application that allows users to type text and receive real-time word predictions.

Conclusion

This milestone confirms the successful loading and exploratory analysis of large text datasets. The findings provide a strong foundation for developing a next-word prediction model and deploying it through a Shiny application. The insights gained from this analysis will guide preprocessing decisions and improve the overall effectiveness of the predictive model.