What is Semantria?
Semantria applies Text and Sentiment Analysis to tweets, facebook posts, surveys, reviews or enterprise content.
Semantria is a tool in the NLP / Sentiment Analysis category of a tech stack.
Why developers like Semantria?
Here’s a list of reasons why companies and developers use Semantria
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- Excel add-in installs and runs directly in your Microsoft Excel
- Concept Matrix and Deep Learning
- Content Discovery
- Named Entity Extraction
- Theme Extraction
- Text Summarization
- Query Categorization
- Facets and Attributes
- Crawling and Automatic Text Extraction
- Wikipedia-based categorization technology
Semantria Alternatives & Comparisons
What are some alternatives to Semantria?
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It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.
Google Cloud Natural Language API
You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage.
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs so you can easily integrate natural language processing into your applications.
It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.