From Brede Wiki
Text mining is computerized automatic text processing, e.g., to cluster papers, classify emails, determine sentiment of text and extract relationships between named entities.
- Co-word analysis
- Literature-based discovery
- Part-of-speech tagging
- Question answering
- Question classification
- Sentiment analysis
- Text negation analysis
- Gensim, Python tool for, e.g., latent semantic analysis
- I2E, text mining toolkit from Linguamatics
- NLTK, natural processing toolkit for Python. Probably the leading NLP toolkit for Python.
- OpenNLP, written in Java
- Textpresso for Neuroscience http://www.textpresso.org/neuroscience/
- Data mining a functional neuroimaging database for functional segregation in brain regions
- Automated recognition of brain region mentions in neuroscience literature
- Mining for associations between text and brain activation in a functional neuroimaging database
- Mining the posterior cingulate: segregation between memory and pain components
- Modeling of activation data in the BrainMap(TM): Detection of outliers
- Application and evaluation of automated semantic annotation of gene expression experiments
- Raul Rodriguez-Esteban (2009). "Biomedical Text Mining and Its Applications". PLoS Computational Biology 5(12): e1000597. doi: 10.1371/journal.pcbi.1000597.
 General science
- Edoardo M. Airoldi, Elena A. Erosheva, Stephen E. Fienberg, Cyrille Joutard, Tanzy Love, Suyash Shringarpure (2010). "Reconceptualizing the classification of PNAS articles". PNAS 107(49): 20899-20904. doi: 10.1073/pnas.1013452107.