"Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text."
Analyzing large amounts of text using software for patterns and trends.
Corpus building: The process of collecting and organizing a large collection of texts for analysis.
Cleaning and normalization: Identifying and correcting common errors such as misspellings or inconsistent formatting.
Text pre-processing: Converting text into a format suitable for analysis, such as tokenization (splitting text into individual words), stemming (reducing words to their base form), and stop word removal (excluding commonly used words like “the” and “a”).
Quantitative methods: Using statistical techniques to analyze large collections of text, such as frequency analysis, sentiment analysis, and topic modeling.
Qualitative methods: Employing techniques such as close reading and content analysis to interpret and analyze individual texts or smaller collections of texts.
Text classification: Categorizing texts based on certain criteria, such as genre or topic.
Machine learning: Using algorithms to teach computers to recognize patterns in text and make predictions based on that data.
Natural Language Processing (NLP): Using computational techniques to understand and analyze human language.
Named Entity Recognition (NER): Identifying and categorizing named entities such as people, places, and organizations.
Visualization: Using charts, graphs, and other visual aids to present data in a way that is easy to understand.
Digital tools: Using specialized software for text analysis, such as R, Python, or Voyant.
Ethics and biases: Understanding the potential biases and ethical concerns that can arise when using digital methods to analyze text.
Sentiment Analysis: Identifying and extracting the underlying emotion or sentiment expressed in a text.
Topic Modeling: A statistical approach to identifying the key topics in a corpus of texts by clustering words or phrases that commonly occur together.
Named Entity Recognition: Identifying and extracting named entities, such as people, places, or organizations, from a text.
Text Classification: Assigning a document to one or more predefined categories based on its content.
Text Summarization: Summarizing the content of a lengthy text, either by extracting key phrases or sentences or by generating a new text that captures the essence of the original.
Network Analysis: Identifying and visualizing patterns of connections between entities or concepts in a text, such as co-occurrence, citation, or collaboration.
Contextual Analysis: Analyzing the cultural, social, historical, or political context in which a text was produced, received, and interpreted.
Corpus Linguistics: Using computational methods to investigate patterns of language use across a large collection of texts or a specific domain of discourse.
Discourse Analysis: Analyzing the structure, content, and use of language in a specific context, such as a conversation, a news article, or a political speech.
Textual Data Mining: Using machine-learning algorithms to discover patterns or insights in large amounts of textual data, often in combination with other types of data.
"Written resources may include websites, books, emails, reviews, and articles."
"High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning."
"According to Hotho et al. (2005) we can distinguish between three different perspectives of text mining: information extraction, data mining, and a knowledge discovery in databases (KDD) process."
"Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling."
"Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics."
"The overarching goal is, essentially, to turn text into data for analysis, via the application of natural language processing (NLP), different types of algorithms and analytical methods."
"The document is the basic element when starting with text mining."
"An important phase of this process is the interpretation of the gathered information."
"Written resources may include websites, books, emails, reviews, and articles."
"High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning."
"We can distinguish between three different perspectives of text mining: information extraction, data mining, and a knowledge discovery in databases (KDD) process."
"Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling."
"Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics."
"The overarching goal is, essentially, to turn text into data for analysis, via the application of natural language processing (NLP), different types of algorithms and analytical methods."
"The document is the basic element when starting with text mining."
"An important phase of this process is the interpretation of the gathered information."
"The overarching goal is, essentially, to turn text into data for analysis."
"Text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling."
"Websites, books, emails, reviews, and articles."