Executive Summary

The objective of this project is to develop a text prediction application that suggests the next word based on words already entered by a user.

The source data consist of three English-language text collections: blogs, news articles, and Twitter posts. This report confirms that the data have been successfully downloaded and loaded, summarizes their main characteristics, and outlines the planned approach for building the prediction algorithm and Shiny application.

The main challenge is balancing prediction accuracy with speed. Because the final application must respond interactively, the prediction model should be compact enough to provide suggestions almost instantly.

Data Loading

The three datasets represent different styles of written English:

Using multiple sources should help the final model capture a broader range of everyday English.

Basic Dataset Characteristics

Summary of the English text datasets
Dataset File_Size_MB Lines Words Longest_Line
Blogs 200.4 899,288 37,546,250 40,833
News 196.3 1,010,242 34,762,395 11,384
Twitter 159.4 2,360,148 30,093,413 140

The datasets contain millions of words and therefore provide enough material to estimate common word sequences. Twitter contains the largest number of individual text records, while blog entries tend to contain substantially more text per record.

Exploratory Analysis

Processing the complete corpus repeatedly would be unnecessarily expensive during early development. Therefore, a reproducible sample is used to examine the vocabulary and most frequent words.

Before constructing a prediction model, the text must be normalized. The main preprocessing steps are:

  1. convert text to lowercase;
  2. remove URLs, numbers, punctuation, and unnecessary symbols;
  3. normalize whitespace;
  4. tokenize text into individual words;
  5. construct sequences of consecutive words.

Most Frequent Words

Twenty most frequent words in the sample
Word Frequency
the 44852
to 24550
and 22881
a 21629
of 19477
in 15125
i 13783
that 9773
for 9326
is 9186
it 8627
on 7001
you 6805
with 6580
was 5899
this 4841
at 4750
as 4675
be 4671
have 4592

As expected for natural English text, a relatively small set of common words accounts for a large proportion of all word occurrences. However, the corpus also contains a very large number of uncommon words.

This is important because storing every possible word combination would make the final model unnecessarily large.

Word Coverage

The following analysis shows how much of the sampled text can be represented by increasingly large portions of the vocabulary.

Vocabulary size versus percentage of text covered
Vocabulary_Fraction Token_Coverage
1% 62.6
5% 80.0
10% 86.7
25% 93.7
50% 97.3

The distribution demonstrates a common property of language: frequent words occur very often, while many words appear only rarely. This suggests that low-frequency terms can be pruned from the final model with relatively little loss of practical prediction ability.

Planned Prediction Algorithm

The final prediction engine will be based on an n-gram language model.

An n-gram represents a sequence of consecutive words. For example, from the phrase:

“I want to go home”

the model can learn relationships such as:

The planned model will primarily use bigrams, trigrams, and four-grams.

When the user enters text, the application will first search for predictions using the longest available context. If no sufficiently reliable match exists, the model will back off to a shorter context.

For example:

Entered text:
I would like to

The system may first search for four-word patterns ending in would like to. If no suitable prediction exists, it can search using like to, and finally only to.

This back-off strategy should provide a practical compromise between prediction quality and coverage.

Model Optimization

The final model must work differently from a large research model because it will run inside an interactive Shiny application.

The following optimizations are planned:

Profanity filtering may also be added so that inappropriate predictions are not displayed to users.

Planned Shiny Application

The final application will provide a simple interface in which a user enters a phrase and immediately receives likely next-word predictions.

A typical interaction would be:

User enters

I am going to

Application predicts

the
be
have

The interface is expected to contain:

The primary design objective will be speed and simplicity. A user should not need to understand the statistical model in order to use the application.

Next Steps

The next stage of the project will focus on:

  1. cleaning and tokenizing the corpus;
  2. generating 2-, 3-, and 4-gram frequency tables;
  3. removing low-frequency sequences;
  4. implementing a back-off prediction algorithm;
  5. measuring prediction accuracy on held-out text;
  6. reducing model size and latency;
  7. integrating the final model into a Shiny application.

Conclusion

Initial exploration confirms that the three English datasets have been successfully loaded and contain sufficient text for developing a next-word prediction model.

The data also show the highly uneven frequency distribution typical of natural language: a relatively small portion of the vocabulary accounts for much of the observed text, while many words and word combinations are rare.

This structure supports the planned approach of using a compact n-gram model with pruning and back-off. The next phase will therefore concentrate on building a model that provides useful predictions while remaining small and fast enough for an interactive Shiny application.