Topic Modeling
Discover topics running through large collections of unstructured texts.
Text analysis in the cloud
MineMyText
Text mining made accessible for social science researchers.
A graphical environment for extracting topics and sentiments from large document collections. No coding required.
MineMyText journal
Methods, tools, and publishing workflows.
Practical reading for researchers and editorial teams working with unstructured text, automated analysis, and AI-assisted publishing.
Explore the latest
Latest articles
Core capabilities
From raw language to an interpretable view.
MineMyText brings the essential stages of computational text analysis into one graphical workflow.
Sentiment Analysis
Quantify the positive and negative emotions expressed in texts.
Natural Language Pre-Processing
Clean noisy texts through stopword removal, parts-of-speech tagging, and lemmatization.
Visualization
Explore topics and sentiments through clear, intuitive visual views.
Applications
Find structure across many kinds of document collections.
Scientific papersAccelerate literature reviews and analyze the content of papers across disciplines.
Corporate reportsStudy financial, corporate responsibility, governance, and other official reports.
NewsTrack how themes and sentiment develop across a changing news corpus.
Social webListen to conversations on social networks, forums, blogs, and other social channels.
E-commerceMine the content and sentiment of product descriptions and customer reviews.
Custom documentsAnalyze text stored in spreadsheet, JSON, and plain-text files.
Documented methods
Algorithms with a research trail.
The original MineMyText service centered on scientifically evaluated approaches that researchers could inspect and cite.
Latent Dirichlet Allocation
Words that co-occur in similar contexts tend to carry related meaning. LDA uses those patterns to discover topics across a document collection and annotate individual documents with topic proportions, without predefined categories or manual labeling.
SentiStrength
SentiStrength estimates the strength of positive and negative emotion in short texts. Its dictionary-based approach also considers negation, booster words, idioms, emoticons, and punctuation, making it useful across many domains.
The original team
From researchers, for researchers.
Stefan Debortoli
Research & Development
Product Management
Dr Oliver Müller
Research & Development
Product Management
Professor Jan vom Brocke
Research
Business Development
Michael Gau
Frontend Development
Server Administration
Contact
Interested in MineMyText for research or commercial work?
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