"Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text."
The use of software to extract insights from large amounts of text data, such as news articles.
Natural Language Processing (NLP): This is the branch of artificial intelligence concerned with the interaction between computers and human language.
Data Preparation: This involves cleaning, preprocessing, and formatting data in preparation for analysis.
Text Mining Techniques: This includes topic modeling, sentiment analysis, and named entity recognition, among others used to identify patterns or structure within textual data.
Machine Learning Algorithms: These algorithms are used to find patterns and relationships within the data and help to build predictive models.
Data Visualization: This includes using various graphical tools, such as charts and graphs, to visually represent the data and insights.
Information Retrieval: This deals with the search and retrieval of relevant information from large and complex data sets.
Statistical Analysis: This involves interpreting the data by analyzing statistical relationships and significance in the data.
Text Classification: This is the process of grouping or categorizing text into predefined categories based on its content.
Clustering Analysis: This is a technique used to identify and group similar data points together based on their characteristics.
Text Summarization: This technique involves creating a shorter version of a text, while still retaining the meaning of the original content.
Data Mining: This involves the exploration and extraction of useful information from large data sets.
Big Data Analytics: This focuses on the analysis of large data sets that cannot be processed using traditional data analysis tools.
Data Warehousing: This is the process of collecting and storing data from various sources for easy access and analysis.
Data Visualization Tools: These are software tools designed to help users create interactive data visualizations that can be used to interpret data more effectively.
Web Scraping: This is the process of extracting data from websites and converting it into a format that can be used for analysis.
Social Media Analysis: This involves analyzing data from various social media platforms to identify trends and patterns in customer behavior and sentiments.
Information Extraction: This involves automatically detecting and extracting structured information from unstructured data sources.
Network Analytics: This is the analysis of the relationships and connections between various entities within a data set.
Cognitive Computing: This involves the use of advanced technologies to simulate human thought processes and decision-making.
Data Governance: This involves creating policies and procedures to ensure that data is accurate, consistent, and secure.
Sentiment analysis: This type of text mining involves analyzing the sentiment of text, such as social media posts, news articles, and reviews, to determine whether the language used is positive, negative, or neutral.
Topic modeling: This type of text mining involves clustering and categorizing large volumes of text data into distinct topics, often used to identify trends or patterns in public opinion.
Named entity recognition (NER): This type of text mining involves identifying and categorizing named entities, such as people, organizations, and locations, mentioned in the text.
Content analysis: This involves analyzing text data to identify themes, sentiments, or messages within the data, often used in social science research and media studies.
Information retrieval: This involves the process of searching through text data to quickly and effectively retrieve relevant information.
Text classification: This involves categorizing text data into specific groups or classes based on predefined criteria, such as keywords or topic themes.
Natural Language Processing (NLP): This is a broader category of text mining techniques that involve using computational methods to understand human language and generate language-based insights.
Spark MLlib: This is a text mining toolkit within the Apache Spark framework, which allows for machine learning models to be built to analyze text data.
Word embeddings: This is a technique that involves representing words as numerical vectors in order to capture the semantic, syntactic, and contextual relationships between them.
Rule-based matching: This involves using predefined rules to extract specific information from text data, such as phone numbers or email addresses.
"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."