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Our technology experts help brands listen to their customers and understand consumer trends by applying AI to online data.
What is the sentiment score

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Our technology experts are specializing in building and deploying customized solutions for text analysis to extract insights and intelligence from unstructured content.

We are offering our expertise to assist you in extracting key pieces of information from conversations, using on-premise or cloud-based technology.

Canada, Toronto area

We deliver cost-effective solutions by using Text analytics capabilities provided by Amazon, Google, IBM or Microsoft - based on your project needs.
We will work with you to define the project scope, and select the platforms and tools that can guarantee the best results at the lowest cost.

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Text analytics applications

  • Social media analysis
  • Product Performance
  • Email Classification
  • Corpus Development
  • Keyword Extraction
  • Data Extraction and Preparation

Development + Deployment

  • Design solution
  • Development Consulting
  • Integrate services from third-party providers
  • Create custom domain-specific models
  • Deployment on the cloud or on-premise
  • Maintaining + enhancing existing solutions

We help integrate Text analytics capabilities into your custom solution deployed on premises or on the cloud

We will run your test data through multiple models and Text analytics platforms, and compare the results to identify the model with highest precision rate, and the platform that can produce results quickly and with minimal costs.
When your application requires increased accuracy for industry or domain-specific taxonomies that existing platforms cannot provide, we'll look into customizing open source libraries to produce the desired results.

Text Analytics APIs

Determine Sentiment API

This capability is useful for detecting positive and negative sentiment in social media, customer reviews, and discussion forums.

Text Content is provided by you, models and training data are provided by the service.

The score of a document's sentiment indicates the overall emotion of a document. The magnitude of a document's sentiment indicates how much emotional content is present within the document, and this value is often proportional to the length of the document.

Interpreting sentiment analysis values

Natural Language API indicates differences between positive and negative emotion in a document, but does not identify specific positive and negative emotions. For example, "angry" and "sad" are both considered negative emotions. However, when the Natural Language API analyzes text that is considered "angry", or text that is considered "sad", the response only indicates that the sentiment in the text is negative, not "sad" or "angry".

Keyphrase Extraction API

This capability is useful if you need to quickly identify the main points in a collection of documents, for categorization, clustering, indexing, search, and summarization; quantifying semantic similarity with other documents; as well as conceptualizing particular knowledge domains.

Key phrase extraction works best when you provide large blocks of text. This is opposite from sentiment analysis, which performs better on smaller blocks of text.

The Keyphrase Extraction API returns the key phrases or talking points and a confidence score to support that this is a key phrase.

The service finds and discards non-essential words, and keeps single terms or phrases that appear to be the subject or object of a sentence.

Entity Recognition API

The Entity Recognition API returns the named entities ("People", "Places", "Locations", "Events", etc.) that are automatically categorized based on the provided unstructured text.

Entity analysis

Entity Analysis provides information about entities in the text, which generally refer to named "things" such as famous individuals, landmarks, common objects, etc.

We can use Named Entity Recognition API for

  • Classifying content for news providers
  • Efficient Search Algorithms
  • Powering Content Recommendations
  • Customer Support - feedback categorization
  • Organizing Research Papers