1 Executive Summary

This report presents a comprehensive text mining analysis using a sample corpus from the tm package. It includes detailed preprocessing steps, exploratory analysis, n-gram frequency analysis, and a modeling plan for predictive text generation. Mathematical foundations for each step are explained to ensure clarity and rigor. The goal is to demonstrate proficiency in handling unstructured text data and preparing it for machine learning applications.

2 Introduction

Text mining is the process of extracting meaningful information from unstructured text data. It involves cleaning, transforming, and analyzing textual content to uncover patterns and insights. This report applies text mining techniques to a corpus of crude oil news articles.

Mathematically, text mining transforms a corpus \(C = \{d_1, d_2, ..., d_n\}\) into a structured representation such as a document-term matrix \(D \in \mathbb{R}^{n imes m}\), where \(n\) is the number of documents and \(m\) is the number of unique terms.

2.1 Project Background

Text data is abundant in today’s digital world. From news articles to social media posts, extracting insights from text is crucial for decision-making. This project demonstrates how to process and analyze text data using R.

2.2 Dataset Overview

We use the built-in crude dataset from the tm package, which contains 20 news articles related to crude oil. Each document is a short article with varying length and vocabulary.

2.3 Objectives

  • Clean and preprocess text data
  • Perform exploratory analysis
  • Analyze unigrams, bigrams, and trigrams
  • Visualize word frequencies
  • Identify challenges and plan modeling strategy

3 Data Loading and Preprocessing

3.1 Sampling Strategy

We use the entire corpus due to its small size. Sampling is not required but can be applied in larger datasets using stratified or random sampling.

# built-in `crude` dataset from the `tm` package
library(tm)
data("crude")
corpus <- crude

3.2 Data Cleaning

The following text preprocessing steps were applied:

  1. Lowercase Conversion Standardize all text to lowercase to ensure uniformity. Mathematically, this reduces the vocabulary size by mapping all uppercase variants to their lowercase equivalent.

  2. Number Removal Remove numeric values as they are not relevant for word prediction in this context. This step prevents numbers from skewing term frequency calculations.

  3. Punctuation Removal Strip punctuation marks to simplify tokenization. Punctuation does not contribute to semantic meaning in unigram/bigram analysis.

  4. Whitespace Normalization Remove extra spaces to ensure clean token boundaries. This avoids empty tokens during tokenization.

  5. Stopword Handling

Important: Stopwords (common words like “the”, “is”, “at”) were NOT removed because they are essential for natural language prediction and context modeling.

This reduces dimensionality and improves signal-to-noise ratio.

# Text cleaning with tm package
corpus <- VCorpus(VectorSource(corpus))
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removeWords, stopwords("english"))
corpus <- tm_map(corpus, stripWhitespace)

3.3 Dataset Summary

# Basic summary of corpus
summary(corpus)
##    Length Class             Mode
## 1  2      PlainTextDocument list
## 2  2      PlainTextDocument list
## 3  2      PlainTextDocument list
## 4  2      PlainTextDocument list
## 5  2      PlainTextDocument list
## 6  2      PlainTextDocument list
## 7  2      PlainTextDocument list
## 8  2      PlainTextDocument list
## 9  2      PlainTextDocument list
## 10 2      PlainTextDocument list
## 11 2      PlainTextDocument list
## 12 2      PlainTextDocument list
## 13 2      PlainTextDocument list
## 14 2      PlainTextDocument list
## 15 2      PlainTextDocument list
## 16 2      PlainTextDocument list
## 17 2      PlainTextDocument list
## 18 2      PlainTextDocument list
## 19 2      PlainTextDocument list
## 20 2      PlainTextDocument list
# Calculate statistics
doc_lengths <- sapply(corpus, function(x) strsplit(as.character(x), "\\s+") %>% lengths())
num_docs <- length(corpus)
avg_length <- mean(doc_lengths)
min_length <- min(doc_lengths)
max_length <- max(doc_lengths)

# Vocabulary size
dtm <- DocumentTermMatrix(corpus)
vocab_size <- length(dtm$dimnames$Terms)

# Create summary table
summary_table <- data.frame(
  Metric = c("Number of Documents", "Average Document Length", "Minimum Document Length", "Maximum Document Length", "Vocabulary Size"),
  Value = c(num_docs, round(avg_length, 2), min_length, max_length, vocab_size)
)

# Display as table
knitr::kable(summary_table, caption = "Dataset Summary Statistics")
Dataset Summary Statistics
Metric Value
Number of Documents 20.00
Average Document Length 122.15
Minimum Document Length 35.00
Maximum Document Length 265.00
Vocabulary Size 951.00

4 Exploratory Analysis

dtm <- DocumentTermMatrix(corpus)
m <- as.matrix(dtm)
# word_freq <- sort(rowSums(m), decreasing = TRUE)
word_freq <- sort(colSums(m), decreasing = TRUE)
word_freq_df <- data.frame(word = names(word_freq), freq = word_freq)
head(word_freq_df, 5)
##          word freq
## oil       oil   85
## said     said   73
## prices prices   48
## opec     opec   42
## mln       mln   31

We compute term frequencies from the document-term matrix. Let \(f_{ij}\) be the frequency of term \(j\) in document \(i\). The total frequency of term \(j\) is \(f_j = \sum_{i=1}^{n} f_{ij}\).

5 Unigram Analysis

Unigrams are individual words. Analyzing their frequency helps us understand which words are most common in the corpus.

Mathematically, if \(w_i\) is a word, its frequency \(f(w_i)\) is computed as: \[ f(w_i) = \sum_{d \in C} {count}(w_i, d) \]

library(tidytext)
library(dplyr)
library(tibble)

# Convert corpus to a clean tibble with character column
text_df <- tibble(text = as.character(sapply(corpus, as.character)))
unigrams <- text_df %>% unnest_tokens(word, text, token="words")
unigram_freq <- unigrams %>% count(word, sort=TRUE)
#head(unigram_freq, 10)

5.1 Key Observations

  • Frequent words include domain-specific terms like “oil”, “prices”.
  • These words provide grammatical structure but less semantic meaning.
  • Content words (nouns, verbs) appear lower in frequency but carry more meaning.
  • Stopword removal enhances clarity.
  • Total unique unigrams: 957

6 Bigram Analysis

Bigrams are pairs of consecutive words. They help reveal contextual relationships and common phrases.

Mathematically: \[ f(w_i, w_{i+1}) = \sum_{d \in C} {count}((w_i, w_{i+1}), d) \]

\(B = \{(w_i, w_{i+1})\}\) is the set of bigrams.

bigrams <- text_df %>% unnest_tokens(bigram, text, token = "ngrams", n = 2)
bigram_freq <- bigrams %>% count(bigram, sort = TRUE) %>% filter(!is.na(bigram))
#head(bigram_freq, 20)

6.1 Key Observations

  • Frequent bigrams often include collocations like “oil prices”, “crude oil”.
  • These phrases provide more semantic meaning than individual words.
  • Total unique bigrams: 1944

7 Trigram Analysis

Trigrams are sequences of three consecutive words. They capture more complex linguistic patterns.

Mathematically: \[ f(w_i, w_{i+1}, w_{i+2}) = \sum_{d \in C} ext{count}((w_i, w_{i+1}, w_{i+2}), d) \]

\(T = \{(w_i, w_{i+1}, w_{i+2})\}\) is the set of trigrams

trigrams <- text_df %>% unnest_tokens(trigram, text, token = "ngrams", n = 3)
trigram_freq <- trigrams %>% count(trigram, sort = TRUE)
#head(trigram_freq, 20)

7.1 Key Observations

  • Trigrams like “crude oil prices” show topic-specific phrases.
  • Useful for predictive modeling and phrase-level analysis.
  • Total unique trigrams: 2158

8 Word Cloud Visualization

library(wordcloud)
library(RColorBrewer)
wordcloud(words = word_freq_df$word, freq = word_freq_df$freq, min.freq = 2,
          max.words = 100, random.order = FALSE, colors = brewer.pal(8, "Dark2"))

Word clouds visualize term frequency. Larger words indicate higher frequency.

9 Data Quality and Challenges

  • Small dataset size limits generalization.
  • Domain-specific jargon may introduce bias.
  • Lack of metadata (e.g., publication date).

10 Considerations for Model Building

  • Model:

    • Use n-gram features for predictive modeling.
    • Character n-grams for handling misspellings and morphology.
    • POS tags or syntactic features for richer context.

  • TF-IDF weighting:

    • consider \(tfidf_{ij} = tf_{ij} \cdot \log( rac{N}{df_j})\).
    • Normalize vectors (L2 norm) for better performance in linear models.

  • Model competancy vs speed Trade-off:

    • Higher-order n-grams and deep models improve accuracy but increase training and inference time.
    • Choose simpler models (e.g., Logistic Regression, Naive Bayes) for large-scale or real-time systems.

  • Memory constraints:

    • Sparse Representations: use compressed sparse row (CSR) matrices for DTMs.
    • Vocabulary Pruning: remove rare terms and limit max features to reduce memory footprint.
    • Dimensionality Reduction: apply PCA, Truncated SVD (LSA), or feature hashing.

  • Probabilistic models (e.g., Naive Bayes):

    • Laplace/Additive Smoothing to handle zero probabilities.
    • Kneser-Ney or Good-Turing for advanced n-gram models.

  • Accuracy vs Size Trade-off:

    • Regularization: L1 (sparse models) vs L2 (better generalization).
    • Model Compression: Quantization or pruning for deployment.
    • Feature Selection: Keep top-k features by mutual information or chi-square.

  • Hyperparameter Tuning:

    • n-gram range: (1,1) vs (1,2) vs (1,3) impacts both accuracy and size.
    • Max Features: Control vocabulary size for speed and memory.
    • Alpha for Naive Bayes, C for Logistic Regression, dropout for deep models.

  • Deep Learning Considerations:

    • LSTM/GRU for sequence modeling.
    • Transformers (BERT) for contextual embeddings.
    • Pre-trained embeddings (Word2Vec, GloVe, FastText) for better generalization.

  • Evaluation and Scalability:

    • Cross-validation for robust performance.
    • Incremental Learning for streaming data.
    • Distributed Training for very large datasets.

11 Next Steps and Modeling Plan

11.1 Planned Approach

The predictive text model will be built using an n-gram language model with the following components:

11.1.1 N-gram Model Architecture

  • Build n-gram frequency tables (already completed in this analysis).

  • Calculate conditional probabilities: P( )

  • Implement backoff strategy:

    • Try trigram first.
    • If not found, back off to bigram.
    • If not found, back off to unigram.
    • Use uniform distribution for unknown words.

11.1.2 Smoothing Techniques

  • Katz Backoff: Discount probability mass for seen n-grams and redistribute to unseen.
  • Stupid Backoff: Simplified approach suitable for large datasets.
  • Good-Turing Smoothing: Adjust frequencies based on frequency of frequencies.

11.1.3 Optimization Strategies

To make the model efficient:

  • Pruning: Remove very low-frequency n-grams.
  • Hashing: Use hash tables for fast lookup.
  • Compression: Store only necessary information.
  • Top-K Prediction: Return only top 3–5 predictions.

11.2 Evaluation Plan

Model performance will be evaluated using:

  • Perplexity: How well the model predicts held-out test data.
  • Accuracy: Percentage of correct top-1 and top-3 predictions.
  • Speed: Response time for predictions.
  • Coverage: Percentage of test queries the model can handle.

11.3 Shiny App Requirements

The final application will:

  1. Accept text input (multiple words).
  2. Predict next word(s) using the trained model.
  3. Display top 3 predictions.
  4. Provide fast, real-time response.
  5. Handle edge cases gracefully.

12 Conclusion

This exploratory analysis has provided valuable insights into the text corpus structure:

  • Large vocabulary with most words appearing infrequently (Zipf’s Law).
  • N-grams capture patterns at different levels of context.
  • Coverage analysis shows we can build practical models with manageable vocabulary.
  • Identified challenges that need to be addressed in model building.

The next phase will focus on building an efficient n-gram model with appropriate smoothing and backoff strategies to create a functional predictive text application.

13 Appendix

All code used in this analysis is available in the R scripts given below:

  • data_loading_preprocessing.R: Data loading and sampling
  • eda_analysis.R: N-gram analysis and visualization
#inspect(dtm[1:5, 1:5])
sessionInfo()
## R version 4.5.1 (2025-06-13 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 22631)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: Asia/Calcutta
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] textdata_0.4.5     stringr_1.5.2      tidyr_1.3.1        tidytext_0.4.3    
##  [5] tibble_3.3.0       dplyr_1.1.4        ggplot2_4.0.0      wordcloud_2.6     
##  [9] RColorBrewer_1.1-3 SnowballC_0.7.1    tm_0.7-16          NLP_0.3-2         
## 
## loaded via a namespace (and not attached):
##  [1] janeaustenr_1.0.0 sass_0.4.10       generics_0.1.4    xml2_1.4.0       
##  [5] slam_0.1-55       stringi_1.8.7     lattice_0.22-7    hms_1.1.3        
##  [9] digest_0.6.37     magrittr_2.0.4    evaluate_1.0.5    grid_4.5.1       
## [13] fastmap_1.2.0     jsonlite_2.0.0    Matrix_1.7-3      purrr_1.1.0      
## [17] scales_1.4.0      jquerylib_0.1.4   cli_3.6.5         rlang_1.1.6      
## [21] tokenizers_0.3.0  withr_3.0.2       cachem_1.1.0      yaml_2.3.10      
## [25] tools_4.5.1       parallel_4.5.1    tzdb_0.5.0        vctrs_0.6.5      
## [29] R6_2.6.1          lifecycle_1.0.4   fs_1.6.6          pkgconfig_2.0.3  
## [33] pillar_1.11.1     bslib_0.9.0       gtable_0.3.6      glue_1.8.0       
## [37] Rcpp_1.1.0        xfun_0.53         tidyselect_1.2.1  rstudioapi_0.17.1
## [41] knitr_1.50        farver_2.1.2      htmltools_0.5.8.1 labeling_0.4.3   
## [45] rmarkdown_2.30    readr_2.1.5       compiler_4.5.1    S7_0.2.0