01Cloud language APIs
Google Cloud Natural Language
Google Cloud API for entity extraction, sentiment analysis, content classification and syntax analysis.
Best for: Quick text analysis on Google Cloud.
Visit Google Cloud Natural Language →Text Data Analytics Solutions
Cloud language APIs, open-source NLP libraries and text analytics platforms that find entities, topics, sentiment and meaning in documents, reviews, support tickets and social posts.
17Platforms & tools
4Categories
5FAQs answered
02Cloud language APIs
Amazon Comprehend
AWS natural language processing service for entities, key phrases, sentiment and topic modelling.
Best for: NLP inside AWS workflows.
Visit Amazon Comprehend →03Cloud language APIs
Azure AI Language
Microsoft's language service for sentiment, entity recognition, summarisation and question answering.
Best for: Language features in Azure applications.
Visit Azure AI Language →04Cloud language APIs
IBM watsonx Natural Language Understanding
IBM service that extracts entities, concepts, categories, emotion and sentiment from text.
Best for: Enterprise text enrichment.
Visit IBM watsonx Natural Language Understanding →05Open-source NLP
spaCy
Industrial-strength Python library for natural language processing with fast pipelines for entities, parsing and classification.
Best for: Production NLP pipelines in Python.
Visit spaCy →06Open-source NLP
NLTK
Python toolkit for teaching and research in natural language processing.
Best for: Learning NLP and quick experiments.
Visit NLTK →07Open-source NLP
Gensim
Python library for topic modelling and document similarity on large text collections.
Best for: Topic models and embeddings.
Visit Gensim →08Open-source NLP
BERTopic
Topic modelling technique that uses transformer embeddings to find readable topics.
Best for: Discovering themes in large text sets.
Visit BERTopic →09Open-source NLP
Stanford CoreNLP
Java toolkit from Stanford for linguistic analysis such as parsing, entities and sentiment.
Best for: Linguistic analysis in Java environments.
Visit Stanford CoreNLP →10Open-source NLP
Hugging Face Transformers
Library of pretrained transformer models for classification, extraction, summarisation and more.
Best for: Using state-of-the-art language models.
Visit Hugging Face Transformers →11Text analytics & feedback platforms
SAS Visual Text Analytics
SAS software for extracting topics, sentiment and entities from text within its analytics platform.
Best for: Text analytics for SAS-based teams.
Visit SAS Visual Text Analytics →12Text analytics & feedback platforms
Medallia
Experience management platform that analyses customer feedback, including text comments and conversations.
Best for: Customer experience programmes.
Visit Medallia →13Text analytics & feedback platforms
Chattermill
Customer feedback analytics platform that groups themes and sentiment across reviews, surveys and tickets.
Best for: Turning feedback into themes.
Visit Chattermill →14Text analytics & feedback platforms
Thematic
Feedback analysis software that finds themes and sentiment in survey responses and reviews.
Best for: Analysing open-ended survey answers.
Visit Thematic →15Text analytics & feedback platforms
Lexalytics
Text analytics and natural language processing software for extracting meaning from unstructured text.
Best for: Enterprise text analytics components.
Visit Lexalytics →16Social listening
Brandwatch
Social listening and consumer intelligence platform that analyses online conversations.
Best for: Brand and market monitoring.
Visit Brandwatch →17Social listening
Sprinklr
Customer experience platform with social listening and analytics across digital channels.
Best for: Enterprise social and care analytics.
Visit Sprinklr →Choosing the right approach
Match your requirement to a starting point, then compare vendors.
| Sentiment and entities from text, fast | A cloud language API such as Google, AWS, Azure or IBM |
|---|---|
| Full control and custom models | spaCy or Hugging Face Transformers |
| Discover themes in many documents | BERTopic or Gensim |
| Understand customer feedback | A feedback platform such as Medallia, Chattermill or Thematic |
| Monitor brands and markets online | Social listening such as Brandwatch or Sprinklr |
| Text trapped in scans or images | OCR first, then text analytics |
Related pages on PeterIndia.net
Neighbouring directories and hubs.
Natural Language ProcessingThe NLP hub.NLP Models for Sentiment AnalysisModels for sentiment tasks.Python Libraries for Sentiment AnalysisLibraries compared.Multimodal Language Models for OCRGet text from images and scans.Audio Data Analytics SolutionsTranscribe speech first.Image Data Analytics SolutionsImages that contain text.Knowledge GraphsStructure extracted entities.Data AnalyticsThe data analytics hub.
Frequently asked questions
Quick answers.
What is text data analytics?
It is turning unstructured text, such as documents, emails, reviews and support tickets, into structured information: entities, topics, sentiment, categories and relationships that can be counted and analysed.
What is the difference between NLP and text analytics?
NLP is the set of techniques for processing language. Text analytics applies those techniques to answer business questions about collections of text.
Do large language models replace these tools?
They can perform many of these tasks and are often used for summarisation and extraction. Dedicated APIs and libraries remain useful for cost, speed, control and repeatable pipelines.
How do I measure sentiment accuracy?
Label a sample of your own text and compare tool output against it. General sentiment models often miss domain slang and sarcasm.
Where do transcripts and OCR output fit?
Speech transcripts and text extracted from images are ordinary text once produced, so the same tools apply. See the audio and image directories.
Descriptions are summaries of each vendor's own positioning, checked against its website on 4 October 2026. Product names and offerings change often, so confirm details on the vendor site before deciding.