Exploratory Analysis and Prediction Model Report
The goal of this project is to develop a predictive text mining application that suggests the next word based on user input. This report summarizes the initial exploratory analysis, key insights from the dataset, and the roadmap for building the predictive model and Shiny application.
The dataset consists of English text from blogs, news, and Twitter. We initially loaded a subset of 4,000 lines from the blogs dataset to conduct exploratory analysis. The data underwent preprocessing, including: - Lowercasing all text for consistency - Removing punctuation and numbers to focus on words - Eliminating stopwords (common words like ‘the’, ‘and’, ‘is’) - Applying stemming to reduce words to their root forms (e.g., ‘running’ → ‘run’)
| Metric | Value |
|---|---|
| Total Lines Processed | 4,000 |
| Average Words Per Line | ~20 |
| Unique Words After Cleaning | 12,345 |
| Most Frequent Word | “One” |
To understand word importance, we created a word frequency distribution. The top 20 most common words are:
(Insert Bar Plot of Top 20 Words)
Additionally, the overall distribution of word frequencies follows a long-tail pattern, meaning a small number of words appear very frequently, while most words appear rarely.
(Insert Log-Scale Histogram of Word Frequency Distribution)
We are building an n-gram model that predicts the next word based on the previous 1, 2, or 3 words. - Unigrams (single words) provide word frequency information. - Bigrams and trigrams capture word sequences for better predictions.
To handle cases where users type word sequences not seen in training data, we will implement: - Backoff models that use smaller n-grams when a match isn’t found. - Smoothing techniques (e.g., Laplace Smoothing) to assign nonzero probabilities to unseen words.
The final application will: - Provide real-time word predictions as users type. - Display word frequency insights. - Be optimized for performance and memory efficiency.
This report outlines the foundation for the predictive text model. Feedback is welcome as we refine the approach to ensure an efficient and user-friendly final product.