Text data mining (TDM) by text analysis, information extraction, document mining, text comparison, text visualization and topic modelling

1. Free Content Analysis Software
2. Software For Content Analysis
3. Content Analysis Software Free Mac
4. Content Analysis Software Free
5. Content Analysis Software Free Mac Operating System

MAXQDA is the #1 content analysis software: Easy to use, identical on Windows and Mac, and relied on by thousands of researchers worldwide. MAXQDA allows you to analyse texts, transcriptions, media files, images, PDFs, and more. Download the free trial today!

• Photosensitive Epilepsy Analysis Tool (PEAT) The Trace Center’s Photosensitive Epilepsy Analysis Tool (PEAT) is a free, downloadable resource for developers to identify seizure risks in their web content and software. The evaluation used by PEAT is based on an analysis engine developed specifically for web and computer applications.
• Word frequency analysis is the simplest form of content analysis. In fact most operating systems (e.g. Unix/Linux, Mac OSX, and recent versions of Windows) have utilities to perform basic word counting and sorting built in. Category Frequency Analysis Content analysis programs almost all allow the user to specify a dictionary.
• MAXQDA is the world-leading software package for qualitative and mixed methods research and the only leading QDA software to offer identical features on Windows and Mac. It is one of the most comprehensive qualitative data analysis programs and is used by thousands of researchers in more than 150 countries.

The search engine extracts automatically texts of different file formats and uses grammar rules (stemming) to index and find different word forms.

On this base and index you can search, review, filter, analyze and mine content with different text mining, analysis, extraction, data mining and clustering methods.

So you can use the search engine not only for information retrieval by full text search to search and find known issues or to get structured data from unstructured data sources or texts by information extraction. It can be used as integrated text mining toolbox for text datamining (TDM) for semi-automated or automated text analysis, document mining, text comparision, text visualization and topic modelling to get useful analysis results even of unknown data sources.

Search and filter the interesting documents

If you don't want to analyze all indexed documents, you can search and filter the context you want to mine and analyze.

Words: Word list and word cloud

The view Words (option of the tab/button Analyze) shows you the words which are contained in the most documents of the results of your search context (documents matching your search query and filters).

If you do not enter a search query and don't use a filters it shows the words which are contained in the most of all indexed documents.

The number shows you how many documents (matching your search query and filters or if no search query or filter of all documents) use this word.

If you click on a word, this word will be added as an additional filter.

The words are visualiszed as a word cloud. The more documents containing the word, the bigger it is in the visualization

Aggregated overviews of extracted structured informations, named entities and concepts for exploratory search (thesaurus based, ontonologies based and machine learning for automatic classification based faceted search)

With the faceted search you can see an aggregated overview for the different facets like paths, concepts, persons, locations or organzations showing, how many documents matching the named entities.

This structure will be generated and facets/fields are valued with data from the following analysis:

• Lists of Named Entities: Listed known named entities like organizations, persons, locations or concepts. They can be managed in plaintext lists, databases, ontologies, thesauri or in the thesaurus user interface for dictionary based or thesaurus based text mining and thesaurus based faceted search
• Annotation & Tagging: Tags from (collaborative) annotations and tagging
• Text patterns (Regular Expressions): Extraction of structured data or data enrichment with text patterns (regular expressions) can extract informations like email-adresses or amounts of money. They are added to facets like Email adresses, From:, To: or money.
• Named entity extraction or Named entity recognition (NER) of even yet unknown entities like persons, organizations or locations by automatic classification of this text parts by machine learning on an annotated training corpus model

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Topic modelling (clustering and differences)

Coming soon (please donate so we can implement this sooner):

Topic modelling (clusters of topics what about documents are)

What are the contents about? What are the most common topics in the whole, selected or filtered document set?

Coocuration (Connected words): Which words occure together (Bigrams/Trigrams/N-Grams)?

What is special in comparision with another text or document set ? See 'Compare text or part of the corpus with other text or part of corpus'.

Similarity ('more like this')

Coming soon (please donate so we can implement this sooner):

Search with a whole document or text as a search query:

If not yet, index your document which should be used as search query.

Search for that document (i.e. by filename).

Find similar text or documents about the same topics by clicking on 'more like this'.

Direct text comparision: Differences of two text versions (visualization of added, deleted or copy pasted parts)

Compare two texts / versions to show differences or same/copied passages or deleted or added words.

Coming soon (please donate so we can implement this sooner):

Document set comparision (show differences like overrepresented terms)

Coming soon (please donate so we can implement this sooner):

Special focus of a text or document set (text corpora) by comparision with other text or document set (text corpora).

Show differences and focal points, core areas and key aspects by comparing word frequencies to find out what concepts or entities are overrepresented in documents in comparison to other documents or text corpus.

Extract text patterns with Regular Expressions (RegEx)

You can extract some structured data i.e. for aggregated overviews, interactive navigation and interactive filters (faceted search), data analysis and data visualization from unstructured text by extraction of the interesting text parts to structured flields, properties or facets by defining text patterns with regular expressions (RegEx) or own regular expressions based enhancer plugins

Advanced text analysis, text mining, document mining and text visualizations

Advanced features like clustering and network analysis and advanced visualizations need more CPU load, more parameters and knowledge and specialized tools for different analysis, so you have to start them manually for your documents or for special search context.

But many advanced text mining tools support only few document formats and data formats and do not optical character recognition (OCR) automatically.

Since this free software is interoperable open source software and uses open standards you are free to integrate additional data enrichment or data analysis plugins or to use other specialized tools additionally and based on the (exportable) text extraction, data enrichment, search and filter results of the search engine.

How to explore and analyse a document collection with external text mining tools?

After automatic extracting, indexing, analysis (i.e. optical character recognition by OCR engines) and enriching (i.e. with Named Entities or extraction of email-addresses) you can do an advanced text analysis, text mining and document mining with this special tools based on an export of all data or an export of search results or filtered results:

• Search and filter/drill down the interesting document set (or do not, if you want to analyze all documents)
• Export this search results to a CSV file. Select the interesting fields like id, title, persons, organzations and mainly the fields content and ocr_t
• Import the CSV in other open source text mining tools and use the extracted text data with natural language processing (NLP) or machine learning (ML), named entities recognition (NER) or classification libraries until some of its advances machine learning methods for text mining are integrated into the user interfaces
• Use their advanced features and views, for example different views from Jigsaw

Free Software and Open Source text analytics and text mining toolkits and platforms or text mining solutions

Alternate Free Software and Open Source text analytics and text mining toolkits or text mining platforms:

Text mining platforms

• Gate - General architecture for text engineering

Open source components for natural language processing (NLP), clustering and classification (machine learning)

Open source frameworks & programming libraries or APIs for natural language processing (NLP), clustering and classification (machine learning):

• Apache Solr (Java based REST-API)
• Elastic search
• Apache UIMA - Unstructured Information Management Architecture for information extraction
• DKPro - Text mining framework (Java and UIMA)
• OpenNLP - Command line tools and Java library
• Python Natural Language Toolkit (NLTK) - Natural language processing library (Python)
• Gensim - Topic modelling programming library (Python)
• Mallet (Java)
• Apache Mahout (Java)
• Apache Spark (Java, but APIs for Pyton, too)
• Apache Stanbol

More: Text Analysis Portal for Research or in Wikipedia list of text mining software

Gwyddion is a modular program for SPM (scanning probe microscopy) datavisualization and analysis. Primarily it is intended for the analysis of heightfields obtained by scanning probe microscopy techniques (AFM, MFM, STM,SNOM/NSOM) and it supportsa lot of SPM data formats.However, it can be used for general height field and (greyscale) imageprocessing, for instance for the analysis of profilometry data or thicknessmaps from imaging spectrophotometry.

Gwyddion provides a large number ofdata processing functions, includingall the standard statistical characterization, levelling and data correction,filtering or grain marking functions. And since the developers are activeSPM users, the program also contains a number of specific, uncommon, odd andexperimental data processing methods they found useful – and you may find themuseful too.

Gwyddion is Free and Open Source software, covered byGNU General Public License.It aims to provide a modular program for 2D data processing andanalysis that can be easily extended by third-party modules and scripts.Moreover, thanks to being free software, it provides the source code todevelopers and users, which makes easier both verification of its dataprocessing algorithms and further program improvements.

Gwyddion works on GNU/Linux, Microsoft Windows, Mac OS Xand FreeBSD operating systems on common architectures. All systems can beused also for development. It has a modern graphical user interface basedon the widely portable Gtk+ toolkit, consistentacross all the supported systems.

News

Free Content Analysis Software

Current versions
Gwyddion:	2.59
libgwyfile:	1.4
gwydump:	2.1
gwyiew:	2.0
threshold:	2.5

2021-07-23:Along with data analysis, Gwyddion also offers a number of modules forgeneration of artificial SPM data (and patterns in general, seeGIMP plug-in replacements).If you want to know how artificial data can be used in SPM, you can readour recent reviewin which we summarised the methods and applications.

2021-06-28:Version 2.59 “Definition Diet” was released. The biggest change was invisible,a major cleanup of module parameter handling. However, it was accompanied withmany small improvements in dozens of modules. See thedetailed news for the complete list of changes.

2021-02-14:We switched TeX equations in the on-line user guide from PNG to SVG. Theynow look cleaner, scale nicely with the page, etc. All non-ancient webbrowsers should have reasonable SVG support – but please report if equations donot display correctly for you.

2021-02-09:Version 2.58 “Plainly Planar” was released. It mainly fixes masked planelevelling, broken in 2.57. However, it includes a few other improvements, forinstance in Renishaw support, a new perspective correction function and a newJapanese translation. See the detailed news forthe full list of changes.

2020

2020-12-28:Version 2.57 “Professing Profiles” was released. Again, there are a bunchof improvements in file format support, but also new profile extraction andcomparison functions, data classification using neural networks and more. Seethe detailed news for the full list ofchanges.

2020-09-17:Several new functions added to Gwyddion in the last two releases implement ideas described in our recent papers.Image terrace fittingand Graph terrace fittingimplement algorithms described in the Si step evaluation paper.The new Correlation length toolwas a repeatedly requested feature. However, it also helps with checking ifimage dimensions are sufficiently larger than the autocorrelation length forroughness measurement – as discussed in a couple of papers,one more technicaland one more high-level.

2020-07-29:To get working Gwyddion packages in Ubuntu you can use theGwyddion-SPM PPAinstead of the broken distro packages(bug 1882441). The PPA also provides more current versions of Gwyddion.

2020-07-12:Gwyddion package in Ubuntu 20.04 (possibly other versions?) is broken, failingto load any modules at startup due to linking issue. Seebug 1882441in Launchpad. At present there is no known workaround aside for compilationfrom source code (as dscribed in the bug report).

2020-06-30:Version 2.56 “Semiscale” was released. The main improvements are in fileformat support, but there is also a new tool for autocorrelation lengthestimation and the usual bunch of bugfixes and smaller improvements. See thedetailed news which contain the complete list ofchanges.

2020-06-08:Version 1.4 oflibgwyfilewas released. This is mainly a bugfix release, which also adds a couple offunctions for object and item copying.

2020-06-02:Version 1.3 oflibgwyfilewas released. The main addition is an interface for GWY files in memorybuffers. The library was also updated to handle new data types introduced inGwyddion 2.53.

2019

2019-11-04:Version 2.55 “Zzz…” was released. It is mainly a bugfix release bringing backZIP support in file import modules. However, there are also a few new volumedata modules or a number of file format support improvements. See thedetailed news for the complete list.

2019-09-22:Version 2.54 was, unfortunately, relased without ZIP support, meaning thata bunch of file formats cannot be loaded: APE DAX, OpenGPS, JPK force data,NanoObserver, NanoScanTech, Olympus POIR, Sensofar PLUx and SPMx. Thisaffects not just MS Windows executables, but ZIP support is silently disabledalso when you compile from source code. We will be releasing 2.55 in severalweeks with a fix. Please use version 2.53 or development snapshots meanwhileif you need to read some of the affected file formats.

2019-09-04:Version 2.54 added twosynthetic datamodules, one directly inspired by an old Yeti'sGIMP plug-in.We updated the replacementinformation to illustrate all the recently added data generators.

2019-08-27:Version 2.54 “A Small Step” was released. It fixes a large number of bugs,but also brings a dozen new modules – data process, file, graph – and varioussmaller improvements. See thedetailed news for the complete list.

2019-06-05:Gwyddion is now available as Flatpak app,a cross-distro way of installing stable bundled applications. See thedownload page for more. In relatednews, the Fedora 30 repository should be finally working.

2019-05-09:We added a paperdescribing some recently added MFM data processing methods to thepublication list.

2019-03-06:Apatchfor version 2.53 was published, fixing Gwyddion being unable to find icons andsimilar data on a Mac.See also Patches.

2019-02-28:Apatchfor version 2.53 was published, fixing a compilation error with GCC 9. Itbreaks compilation with other GCC versions, so only apply it when necessary.See also Patches.

2019-02-28:Version 2.53 “OMGOMP” was released. It adds OpenMP parallelisation for a bunchof data processing operations. There are also improvements in basic tools,a few new modules and some bug fixes as usual. See thedetailed news for the complete list.

2018

2018-11-21:We would like to invite you to the next biannualSPM workshopwhich will take place in Lednice (Czech Republic), 27–29 March 2019.Registration will open in November (or December).

2018-11-15:Apatchfor version 2.52 was published, fixing a build failure on macOS.See also Patches.

2018-11-14:Version 2.52 “Thoroughly Transferred” was released. It brings a few new fileimport modules, some improvements mainly in MFM and volume data processing andquite a few bug fixes. See the detailed news forthe complete list.

2018-10-17:Since Debian is – unfortunately – is going to terminate GTK+2 support, notethat it will become rather difficult to install and use Gwyddion in Debian.Not much can be done except the recommendation to use a Linux distributionwhich values backward compatibility instead.

2018-06-26:Version 2.51 “Astronomical Almanac” was released. It brings over a dozen newmodules, many of them MFM and volume data processing-related. See thedetailed news for the complete list ofimprovements and bug fixes.

2018-04-11:Code revisions 20955–20958 were lost due to a SF.net outage. The code itselfhas been recommitted, however, the new revision history is not identical. Ifyou have a working copy updated to the lost revisions, you may be getting errormessages from svn. Please assume your working copy is toast and check outa fresh one if it happens.

2018-03-16:We are happy to announce the publication of the second edition of the bookQuantitative Data Processing in Scanning Probe Microscopyby Elsevier. It includes descriptions of many ideas in (and behind) Gwyddion,as well as basics of different physical quantities measurements andinterpretation using Scanning Probe Microscopy. It is accompaniedwith a set of freely available sample data coveringvarious types of SPM measurements.

2018-02-02:Version 2.50 “Triangular Trap” was finally released. See thedetailed news for the complete list ofnew modules, file format support improvements and other changes.

Software For Content Analysis

2017

2017-09-17:Yeti's talk Gwyddion – Open source software for SPM data analysiswas added to presentations.

Content Analysis Software Free Mac

2017-08-28:Apatchfor version 2.49 was published, fixing build problems when sed is BSD sed,i.e. on Mac OS X and BSD systems.See also Patches.

2017-08-15:Version 2.49 “Window to the West” was released. As usual, it brings a bundleof new modules and various improvements – and also module bundles. The mostnoticeable changes are, nevertheless, a new much nicer icon set by Felix Klingand a better widget for adjusting the bazillion parameters of variousalgorithms. See the detailed newsfor the complete list of changes.

Content Analysis Software Free

2017-08-12:Petr's talk Gwyscan – library for smart scanning paths aboutgwyscanwas added to presentations. See also therelated paper.

Thanks

Content Analysis Software Free Mac Operating System

Gwyddion development is supported byDepartment of Nanometrology, Czech Metrology Institute.