The internet is constantly changing thanks to the ever-changing demands of users. As such, companies have to constantly play catch-up with their constantly changing needs and demands from visitors in providing engaging content. Content analytics software helps companies’ marketers and publishers to give insightful data that will help determine the future of their content.
- Content Analysis Examples
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- Pricing for content analytics software can vary from free to $150,000 per year or more. The software is priced according to its features available and its target audience. Marketers and publishers from small companies will benefit more from a free or low-cost content analytics software while the same people from bigger companies will benefit.
KH Coder is a free software for qualitative data analysis, quantitative content analysis, and text mining for MAC and Windows. The tool provides a variety of statistical analysis functions by using MySQL, ChaSen, and R as back-end tools. Firebird is a completely free and open source software for commercial purposes as well. PostgreSQL is a database which will allow you to create custom data types and query methods. MongoDB is a document database. Cubrid is a relational database management system and provides enterprise-grade features.
TOC:
15+ Content Analytics Software
1. Semrush
2. Google Analytics
- Content Analysis Examples
- 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
- 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
- Gate - General architecture for text engineering
- 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
Google Analytics allows marketers and publishers to measure their content visibility across Google’s platforms and third-party partner platforms that can measure engagement with various content such as Flash, video, social media networks, and many more. Google’s parent company Alphabet has 135,301 employees and earns annual revenue of $55.31 billion.
3. Semrush
Semrush provides “measurable results” that help marketers and publishers to elevate their content visibility with SEO software, content marketing tools, and social media marketing available in their software. The company has 680 employees and earns annual revenue of $98.6 million.
4. Contently
Contently helps content marketers create better content faster with their smart software that combines expert content strategy, global talent network, and enterprise content marketing platform that can generate content success. The company has 183 employees and earns annual revenue of $45.8 million.
5. Bitly
While Bitly is mainly a URL shortener software, it also provides tools that people can use for content analysis with its detailed insights data available. The company has 242 employees and earns annual revenue of $35.1 million.
6. Rebrandly
Rebrandly main’s feature may be a custom-branded URL shortener with brand management tools, the software also includes a helpful analytical data tool that can help marketers and publishers study their visitors that interacted with their content through their generated shortened links. The company has 74 employees and earns annual revenue of $18.5 million.
7. Chartbeat
Chartbeat helps marketers and publishers improve their engagement with audiences with their software that offers real-time content analytics tools. The company has 88 employees and earns annual revenue of $12.8 million.
8. Parse.ly
Parse.ly wants to make content analytics easy with its tailored tools that can bring meaningful results that can help make decisions on their future content. The company has 55 employees and earns annual revenue of $8 million.
9. Matomo
Describing themselves as the alternative to Google Analytics, Matomo wants their content analytics software that prioritizes the protection of their client’s data, as well as their customers’ privacy that still ensures impactful results and guarantees their clients with 100 percent ownership of their data. The company has 19 employees and earns annual revenue of $2.8 million.
10. BL.INK
BL.INK empowers marketers and publishers with their enterprise-level features that include content analytics tools that are powerful with its multi-device support. The company has 8 employees and earns annual revenue of $1.2 million.
FAQs
How can content analytics software help dictate the future content of marketers and publishers?
The insights data provided by the content analytics software can help marketers and publishers determine what’s popular about their content. What are the popular keywords that viewers are interested in? How do they view such content? From their phones? Laptops? Download mac os mavericks dmg google drive. Tablets, perhaps? By having insights data answer these questions, marketers, and publishers will now then optimize their future content based on the data they have.
Is it easy to understand insight data on content analytics software if someone is not a marketer nor a publisher?
It might take some practice for anyone who is not a marketer or publisher to familiarize themselves with reading insight data. But if they are able to know what the data categories meant and what they do, users should be able to read and understand the data just fine. It’s best, however, to have a data scientist or anyone who is an expert in reading data to help better understand the insight data which they may offer advice based on their expertise.
How much does content analytics software cost?
Pricing for content analytics software can vary from free to $150,000 per year or more. The software is priced according to its features available and its target audience. Marketers and publishers from small companies will benefit more from a free or low-cost content analytics software while the same people from bigger companies will benefit more from a high-cost content analytics software.
Dealing with so much data and numbers can be overwhelming. With the content analytics software, analyzing data on content interaction doesn’t have that much of a heavy task as it automatically provides the data that can help marketers and publishers make great informed decisions on their content.
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Text data mining (TDM) by text analysis, information extraction, document mining, text comparison, text visualization and topic modelling
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:
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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. Android tv emulator for mac computer. 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:
Content Analysis Software Free Mac
Free Software and Open Source text analytics and text mining toolkits and platforms or text mining solutions
Content Analysis Software Free Mac Operating System
Alternate Free Software and Open Source text analytics and text mining toolkits or text mining platforms:
Text mining platforms
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):
More: Text Analysis Portal for Research or in Wikipedia list of text mining software