Text Mining

How Text Mining Works

[Unstructured Text] --> | 1. Text Preprocessing | --> [Cleaned Text] --> | 2. Feature Extraction | --> [Structured Data] --> | 3. Pattern Recognition | --> [Actionable Insights]
        (Input)         | (Tokenization, etc.)  |                    |      (TF-IDF, etc.)     |                     |      (ML Models)       |          (Output)

Core Formulas and Applications

Example 1: Term Frequency-Inverse Document Frequency (TF-IDF)

TF-IDF is a numerical statistic that reflects how important a word is to a document in a collection or corpus. It is widely used in information retrieval and text analysis for feature extraction, helping to filter out common words and emphasize more meaningful ones.

TF-IDF(t, d, D) = TF(t, d) * IDF(t, D)
where IDF(t, D) = log(N / (count(d in D : t in d)))

Example 2: Cosine Similarity

Cosine Similarity measures the cosine of the angle between two non-zero vectors in a multi-dimensional space. In text analysis, it is used to determine how similar two documents are by comparing their TF-IDF vector representations, regardless of their size.

Similarity(A, B) = (A . B) / (||A|| * ||B||)

Example 3: Naive Bayes Classifier

The Naive Bayes algorithm is a probabilistic classifier based on Bayes’ theorem with a strong assumption of independence between features. It is commonly used for text classification tasks like spam detection or sentiment analysis due to its simplicity and efficiency with high-dimensional data.

P(c|x) = (P(x|c) * P(c)) / P(x)

Practical Use Cases for Businesses Using Text Mining

• Customer Feedback Analysis: Automatically process and categorize customer reviews, support tickets, and survey responses to identify key themes, sentiment, and areas for improvement without manual effort.
• Competitive Intelligence: Monitor and analyze news articles, press releases, and social media mentions of competitors to track their strategies, product launches, and market positioning.
• Risk Management and Compliance: Scan legal documents, contracts, and internal communications to identify potential risks, ensure regulatory compliance, and flag non-compliant language or clauses automatically.
• Fraud Detection: Analyze insurance claims, financial reports, and transaction descriptions to identify unusual patterns, suspicious language, or anomalies that may indicate fraudulent activity.

Example 1: Sentiment Analysis

Input: ["The service was excellent!", "I am very disappointed.", "The product is okay."]
Process:
1. Tokenize and clean text.
2. Assign sentiment scores to words (e.g., excellent: +1, disappointed: -1, okay: 0).
3. Aggregate scores for each document.
Output: [Positive, Negative, Neutral]
Business Use Case: A company tracks real-time customer sentiment on social media to quickly address negative feedback and identify popular product features.

Example 2: Topic Modeling

Input: Collection of news articles.
Process:
1. Preprocess text and create document-term matrix.
2. Apply Latent Dirichlet Allocation (LDA) algorithm.
3. Identify clusters of co-occurring words.
Output: 
Topic 1: [finance, market, stock, trade]
Topic 2: [sports, game, team, score]
Topic 3: [election, government, vote]
Business Use Case: A media company uses topic modeling to automatically categorize articles and recommend relevant content to readers.

🐍 Python Code Examples

This example demonstrates basic text preprocessing using the NLTK library, including tokenization, stop-word removal, and stemming.

import nltk
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize

nltk.download('punkt')
nltk.download('stopwords')

text = "Text mining is a fascinating field that involves processing and analyzing large text datasets."

# Tokenization
tokens = word_tokenize(text.lower())

# Stop-word removal
stop_words = set(stopwords.words('english'))
filtered_tokens = [w for w in tokens if not w in stop_words and w.isalpha()]

# Stemming
stemmer = PorterStemmer()
stemmed_tokens = [stemmer.stem(w) for w in filtered_tokens]

print(stemmed_tokens)

This code snippet shows how to use scikit-learn to perform TF-IDF vectorization on a small corpus of documents, converting text into a numerical feature matrix.

from sklearn.feature_extraction.text import TfidfVectorizer

documents = [
    "The cat sat on the mat.",
    "The dog chased the cat.",
    "The mat was on the floor."
]

# Create a TfidfVectorizer object
vectorizer = TfidfVectorizer()

# Generate the TF-IDF matrix
tfidf_matrix = vectorizer.fit_transform(documents)

# Print the feature names and the matrix
print(vectorizer.get_feature_names_out())
print(tfidf_matrix.toarray())

Types of Text Mining

• Information Extraction: This technique involves identifying and extracting specific pieces of information, such as names, dates, locations, or company names, from unstructured text. It transforms free text into structured data by pinpointing key entities and their relationships, making the information accessible for databases and analysis.
• Sentiment Analysis: Also known as opinion mining, this method determines the emotional tone behind a body of text. It is commonly used to classify text as positive, negative, or neutral. Businesses apply it to gauge customer satisfaction from reviews, social media comments, and survey responses.
• Topic Modeling: This approach is used to discover abstract topics that occur in a collection of documents. Algorithms like Latent Dirichlet Allocation (LDA) scan a set of documents and automatically group word clusters and similar expressions that best characterize a set of documents.
• Text Summarization: This type of text mining automatically creates a short, coherent, and fluent summary of a longer text document. It condenses the source text into a brief version containing the most important points, which is useful for processing news articles, scientific papers, and long reports.
• Document Classification: This is the process of assigning one or more predefined categories to a document. It is a supervised learning task where a model is trained on labeled examples to automatically categorize new, unseen documents, such as sorting emails into folders or classifying support tickets by issue type.