QCAmap Description
QCAmap is an open-access web application that allows systematic text analysis in scientific projects using the techniques of qualitative content analysis. QCAmap can also be used in research projects, e.g. QCAmap can be used in research projects in e.g. sociology, sociology and education. It can analyze small and large amounts text material and images from interviews, group discussions and observation protocols. Qualitative content analysis is strictly a rule-based procedure that includes qualitative steps (assignment and analysis of category frequencies) and quantitative steps. A web-based interactive software package has been developed that guides you through the various techniques of Qualitative Content Analysing. The software is available for free. All your existing projects have been migrated to the new version. You can continue using the software as usual.
QCAmap Alternatives
Dovetail
Analyze data, share insights, and build your research library. Find opportunities and be a hero within your team. You can find patterns using a variety qualitative research methods, unstructured data, or video files. Dovetail is an analysis program you will love to use. Dovetail can be used to identify patterns in interviews, usability testing, surveys responses, and many other areas. Drag & drop intuitive controls make it easy to organize tags into a hierarchy. You can also add global tags to your project. You can transform qualitative data into quantitative data by adding highlights and visualize your work using a variety of charts. To add tags, simply highlight text and select the text. Transcribe video recordings to find patterns in interviews, usability tests, survey answers, and many other things. Turn qualitative data into quantitative data. You can filter, segment, and chart themes across transcripts, interview notes, survey responses, among other things.
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TextRazor
TextRazor API allows you to extract and understand the Whos, Whats, Whys and Hows from your news stories with unparalleled accuracy and speed. Entity Extraction, Linking, and Disambiguation. Keyphrase extraction. Automatic Topic Tagging, Classification. All this in 12 languages. Deep analysis of your content allows you to extract Relationships, Typed Dependencies between words, and Synonyms. This allows for powerful context-aware semantic applications. Rapidly extract custom products and companies, and create problem-specific rules to tag your content with your own categories. TextRazor provides a cloud-based or self-hosted text analysis platform. Our combination of state-of-the art natural language processing techniques and a vast knowledgebase of real-life facts allows us to quickly extract the value from your documents.
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Gate
You're in the right spot if you need to solve a problem related to text analysis or language processing. GATE is an open-source software toolkit that can solve almost any text processing problem. It is supported by a large and well-established community of users, educators, students, scientists, and developers. It is used by multinational corporations, SMEs and universities worldwide. It boasts a top-notch team of language processing developers. GATE is free and open-source software. Users can get support via GATE.ac.uk from both developers and users, or commercially from our industrial partners. Our team is more than twice the size of comparable projects. Many of these are integrated with GATE2. GATE development3 has received more than EUR5 million. Our goal is to ensure that this money continues to be a good investment for all GATE users.
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Google Cloud Natural Language API
Machine learning can provide insightful text analysis that extracts, analyses, and stores text. AutoML allows you to create high-quality custom machine learning models without writing a single line. Natural Language API allows you to apply natural language understanding (NLU). To identify and label fields in a document, such as emails and chats, use entity analysis. Next, perform sentiment analysis to understand customer opinions and find UX and product insights. Natural Language with speech to text API extracts insights form audio. Vision API provides optical character recognition (OCR), which can be used to scan scanned documents. Translation API can understand sentiments in multiple languages. You can use custom entity extraction to identify domain-specific entities in documents. Many of these entities don't appear within standard language models. This allows you to save time and money by not having to do manual analysis. You can create your own machine learning custom models that can classify, extract and detect sentiment.
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Pricing
Free Version:
Yes
Integrations
No Integrations at this time
Company Details
Company:
QCAmap
Headquarters:
Germany
Website:
www.qcamap.org/ui/home
Media
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Product Details
Platforms
SaaS
Type of Training
Documentation
Customer Support
Online
QCAmap Features and Options
Qualitative Data Analysis Software
Annotations
Collaboration
Data Visualization
Media Analytics
Mixed Methods Research
Multi-Language
Qualitative Comparative Analysis
Quantitative Content Analysis
Sentiment Analysis
Statistical Analysis
Text Analytics
User Research Analysis
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