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Text mining: Applications & Art
Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles.…
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Text mining topic overview
The analysis highlights Applications and Art as prominent areas in the source structure around Text mining.
Related topics
132
Source areas
7
Connected nodes
141
Extracted relationships
163
Concept neighborhoods
56
Bridge connections
141
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Applications · 55 topics
Overview · 33 topics
Intellectual property law · 17 topics
Text analysis processes · 11 topics
Text analytics · 9 topics
Implications · 5 topics
Software · 2 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Text mining connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Text mining shows recurring relationship patterns in the source. For example, Text mining → Ananiadou, Applications, Artech House Books, Biology, Biomedicine, Boca Raton, Building, Cambridge, Cambridge University Press, Charles River Media, Classification, Clustering, CRC Press, CRM, Damerau, Delen, DM Review, Edition, Editor, Editors Another extracted example is Text mining → Academic, Additionally, All, Coussement, Definition, DTD, For, For Python, Gensim, GoPubMed, Governments, Health's, IBM, In, Internet, It, Journal Publishing Document Type, Legal, Many, Microsoft. Use these groups to spot repeated connection types before inspecting the individual relationships.
Text mining
Top relations
related to Sources· 59
Text mining → Ananiadou, Applications, Artech House Books, Biology, Biomedicine, Boca Raton, Building, Cambridge, Cambridge University Press, Charles River Media, Classification, Clustering, CRC Press, CRM, Damerau, Delen, DM Review, Edition, Editor, Editors
has application· 37
Text mining → Academic, Additionally, All, Coussement, Definition, DTD, For, For Python, Gensim, GoPubMed, Governments, Health's, IBM, In, Internet, It, Journal Publishing Document Type, Legal, Many, Microsoft
related to Situation in the United States· 9
Text mining → America, As, For, Google Book, Google's, Israel, South Korea, Taiwan, US
related to External links· 7
Text mining → Automatic Content Extraction, Linguistic Data Consortium Archived, Marti Hearst, NIST, October, Wayback MachineAutomatic Content Extraction, What Is Text Mining
related to Situation in the United Kingdom· 7
Text mining → Hargreaves, However, In, Information Society Directive, It, Japan, UK
related to Implications· 6
Text mining → Additionally, For, In, Now, Text, Until
related to Text analytics· 5
Text mining → It, Ronen Feldman, Text, The, These
see also· 2
Text mining → Concept, Named
related to Software· 1
Text mining → Text
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Text mining relationships Subject–Predicate–Object triples
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 163 structured relationships around Text mining. Examples in this analysis include statistical pattern learning → instance of → High-quality information is typically obtained by devising patterns and trends by means and telephone numbers → instance of → Features. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
statistical pattern learning
instance of
High-quality information is typically obtained by devising patterns and trends by means
0.80
text
telephone numbers
instance of
Features
0.80
text
e-mail addresses
instance of
Features
0.80
text
quantities
instance of
Features
0.80
text
tokenization
instance of
a casual personal text for the purpose of psychological profiling etc.Pre-processing usually involves tasks
0.80
text
filtering
instance of
a casual personal text for the purpose of psychological profiling etc.Pre-processing usually involves tasks
0.80
text
stemming
instance of
a casual personal text for the purpose of psychological profiling etc.Pre-processing usually involves tasks
0.80
text
the life sciences
instance of
and to support scientific discovery in fields
0.80
text
bioinformatics
instance of
and to support scientific discovery in fields
0.80
text
Internet news
instance of
especially monitoring and analysis of online plain text sources
0.80
text
blogs
instance of
especially monitoring and analysis of online plain text sources
0.80
text
etc. for national security purposes
instance of
especially monitoring and analysis of online plain text sources
0.80
text
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Text mining bring nearby vocabulary together. In this analysis, examples include Text, Data and Information. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Text mining
Text
Data
Information
Analysis
Use
Used
Applications
Software
Content
Extraction
Sentiment
Intelligence
text mining
Text
Data
Information
Analysis
Use
Used
Intelligence
Large
Applications
Software
Content
Extraction
information
Text
Mining
Extraction
Large
Content
Learning
Analysis
Search
Sentiment
Software
Textual
Used
information extraction
Learning
Text
Mining
Document
Sentiment
Extraction
Information
Large
Content
Analysis
Search
Software
data mining
Text
Analysis
Data
Mining
Information
Extraction
Analytics
Textual
Content
Use
Business
Intelligence
structured data
Analysis
Text
Mining
Extraction
Analytics
Textual
Content
Information
Business
Database
Involves
Include
sentiment analysis
Data
Text
Analysis
Sentiment
Content
Document
Software
Techniques
Methods
Analytics
Applications
May
information retrieval
Text
Mining
Extraction
Large
Content
Learning
Analysis
Search
Sentiment
Software
Textual
Used
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Text mining, one of the stronger structural bridges in this analysis connects Text mining with Applications. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Text mining
Nodes142
Edges141
Triples163
Avg. degree1.99
Density0.014085
Components1
Source & methodology
TTTA analyzes the structure around Text mining to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications & Art, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
