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