Major Techniques in Text Mining

Text mining extracts useful patterns and knowledge from unstructured text such as emails, reviews, news and documents.

Text preprocessing (first step)

Tokenization, lower-casing, stop-word removal, stemming/lemmatization (running to run), and spelling correction.

1. Document representation

• Bag of words: each document is a vector of word counts, ignoring order.
• Vector space model (VSM): documents and queries are vectors in term space; similarity is measured by cosine similarity.
• N-grams: sequences of n words capture some word order.
• Embeddings (word2vec, BERT): dense vectors that capture meaning.

2. Term weighting

Gives more importance to informative words.

• TF = (count of term in document) / (total words in document). Example: 4 occurrences in 100 words gives TF = 0.04.
• IDF = log(N / df), where N is the number of documents and df the number containing the term.
• TF-IDF = TF x IDF: high for a word frequent in one document but rare overall.

3. Text classification

Assign documents to predefined categories (supervised): spam filtering, sentiment analysis, topic labelling.

• Methods: Naive Bayes, SVM, KNN, decision trees, neural networks.

4. Text clustering

Group similar documents without labels (unsupervised): grouping news stories or support tickets.

• Methods: K-means on TF-IDF vectors, hierarchical clustering, topic models (LDA).

5. Information extraction

Pull structured facts out of text.

• Named entity recognition (people, places, organizations), relation extraction (who works for whom), event extraction; filling templates and databases.

Challenges

Challenge	Problem	Remedies
High dimensionality	Vocabulary can be tens of thousands of terms; vectors are very sparse; slow and prone to overfitting (curse of dimensionality)	Stop-word removal, stemming, feature selection (chi-square, information gain), dimensionality reduction (LSA/SVD, PCA), embeddings
Semantic ambiguity	Polysemy (one word, many meanings, e.g. "bank"), synonymy (many words, one meaning), context, idioms, sarcasm, negation	Word sense disambiguation, ontologies and WordNet, latent semantic analysis, contextual embeddings, part-of-speech tagging

Other issues: multiple languages, noisy and informal text, very large corpora.

Conclusion

Text mining converts raw text into numeric representations (VSM with TF-IDF), then applies classification, clustering and extraction. Its main difficulties are the huge sparse feature space and the ambiguity of natural language, which are handled with dimensionality reduction and semantic methods.