NLP is a crucial component of text mining that deals with understanding and processing human language. It includes text preprocessing tasks like parsing, tokenization, stemming and lemmatization, and feature extraction tasks like word embeddings, bag-of-words and part-of-speech tagging. NLP techniques prepare text data for further analysis and enable the extraction of meaningful information.

ARM discovers interesting associations and relationships between words or items within text data. This can be used to identify co-occurring terms, frequent patterns, and potential correlations.

Topic tracking algorithms identify and monitor the emergence, evolution, and decline of topics within a stream of text data over time. This is useful for understanding trends and shifts in public opinion, news cycles, and research areas.

Concept linkage aims to identify and connect related concepts within text data by analyzing semantic relationships and co-occurrences. This helps build knowledge graphs and understand the connections between different ideas.

Information Extraction focuses on extracting specific information and structured data from unstructured text. This includes tasks like named entity recognition, relationship extraction, and event detection.

IR deals with searching and retrieving relevant information from large text collections based on user queries. This involves techniques like indexing, ranking, and relevance feedback.

Information visualization techniques represent text data and insights visually through charts, graphs, and interactive dashboards. This helps users understand complex information and discover hidden patterns more easily.

Text summarization algorithms automatically generate concise summaries of longer text documents while preserving the key information and main points.

PageRank is a ranking algorithm used by search engines to measure the importance of web pages based on the number and quality of links pointing to them.

TextRank is a graph-based ranking algorithm used to identify the most important sentences or keywords within a text document based on their connections and co-occurrences.

Classification algorithms can assign text documents or textual data points to predefined categories based on their features and previously labeled training data. This can be used for tasks like sentiment analysis, spam detection, and topic categorization. Example techniques include:

• Decision Trees: A decision tree contains nodes and edges (leaves and branches), where the outcome of a question asked at each node determines the edge to follow. Sequential decision trees ask questions one at a time, following a specific path to reach a classification. In contrast, parallel decision trees consider multiple features simultaneously, potentially reaching a classification faster but potentially sacrificing some accuracy.
• Artificial Neural Networks (ANN): Artificial neural networks can be applied to classification problems by learning how to label data from input training data. A trained model can then autonomously label new input data.

Unsupervised clustering algorithms may be used to group similar text documents or other data points (for instance named entities) into clusters without requiring pre-labeled data. This helps identify hidden patterns and structures within the data. Example techniques include:

• K-Means Algorithm: This algorithm partitions data points into K pre-defined clusters by minimizing the distance between each point and its assigned cluster center.
• Hierarchical Agglomerative Clustering (HAC): This algorithm builds a hierarchy of clusters by successively merging or splitting clusters based on their similarity.