Text mining is the process of automatically discovering patterns and trends in unstructured natural language text that would be difficult to find manually. It is applied across every domain where text data accumulates: analyzing customer reviews, surveying public opinion on social media, tracking trends in academic literature, and classifying call center inquiries.

The basic text mining workflow has four stages. First, preprocessing cleans the text by removing HTML tags, normalizing symbols, and standardizing spelling variations. Second, tokenization splits sentences into individual words and assigns part-of-speech tags. For languages like Japanese and Chinese that do not use spaces between words, dedicated tokenizers (such as MeCab or spaCy for Japanese) are essential. Third, feature extraction converts text into numerical vectors using techniques like TF-IDF or word embeddings. Fourth, analysis and visualization apply methods such as clustering, classification, and topic modeling to the vectorized data.

Frequency analysis is the simplest text mining technique. It tallies word occurrences across a corpus and identifies the most common terms. However, function words like "the," "is," and "of" (stop words) are frequent but carry no meaningful information and must be filtered out. Frequency analysis results are often visualized as word clouds, providing an intuitive overview of the text's dominant themes. Text mining books on Amazon cover these foundational techniques in detail.

Co-occurrence analysis detects patterns where specific words appear together in the same context. For example, if "battery" and "life" frequently co-occur in product reviews, battery life is clearly a key concern for users. Visualizing co-occurrence networks reveals the relational structure between terms across the corpus.

Sentiment analysis classifies text as positive, negative, or neutral. "This product is amazing" is classified as positive, while "I will never buy this again" is classified as negative. Sentiment analysis in languages with rich indirect expression (such as Japanese, where "なかなかですね" can be either a compliment or sarcasm depending on context) tends to be less accurate than in English, where sentiment markers are more explicit.

Character counting connects to text mining at the preprocessing stage, where character and word count statistics serve as foundational data. Document length (character count) is an effective feature for classification models; in spam detection, for instance, unusually short or extremely long messages serve as discriminating signals. N-gram analysis operates at both the character level (character N-grams) and the word level (word N-grams), enabling multi-layered pattern analysis from character-level sequences to semantic-level patterns.