IBM Watson Message Insights

Use IBM Watson to surface actionable insights from the content of messages

IBM Watson Message Insights uses natural language processing technology and machine learning algorithms to extract semantic meta-data from content, such as information on people, places, companies, topics, and languages. Watson surfaces signals from the unstructured message text to help your team get a better understanding of what the customer is saying, feeling and thinking at scale. Watson helps you take action by quickly transforming vast numbers of messages into insights on how people feel about your product, campaign, offer or service.

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  • Ranked Relevance

    A relevance score is calculated for each keyword, entity and concept based on statistical analysis, and the results are returned sorted by relevancy. Use the relevance score to determine the word's relative importance to the message.

  • Coreference Resolution

    IBM Watson Message Insights can resolve the coreferences (i.e. he, she, the company, etc.) into detected entities. Watsons powerful technology understands pronouns and the specific entities they link to.

  • Sentiment Analysis

    IBM Watson Message Insights provides the ability to extract entity and keyword targeted sentiment. Using sentiment analysis can help identity the content that refers to an entity or keyword in a positive or negative manner.

  • Language Support

    IBM Watson Message Insights supports sentiment analysis for content written in English, French, Italian, German, Portuguese, Russian and Spanish, with additional languages in development.

Sample annotation responses

IBM Watson Message Insights API Response JSON

The IBM Watson Message Insights Add-on leverages the Watson TextCombinedData call which performs analysis on a given text document. APIs in the combined call include Keyword Extraction, Entity Extraction, Concept Tagging and Targeted Sentiment.

Response Format (JSON):
   "status": "REQUEST_STATUS", 
   "language": "DOCUMENT_LANGUAGE",
   "totalTransactions": "TRANSACTIONS_CHARGED",
   "title": "DOCUMENT_TITLE", 
   "keywords": [ 
          "text": "DETECTED_KEYWORD",
          "relevance": "DETECTED_RELEVANCE",
          "sentiment": {
              "type": "SENTIMENT_LABEL",
              "score": "SENTIMENT_SCORE",
              "mixed": "SENTIMENT_MIXED"
   "concepts": [ 
        "text": "DETECTED_CONCEPT",
        "relevance": "DETECTED_RELEVANCE",
        "website": "WEBSITE",
        "geo": "LATITUDE LONGITUDE",
        "dbpedia": "LINKED_DATA_DBPEDIA",
        "yago": "LINKED_DATA_YAGO",
        "opencyc": "LINKED_DATA_OPENCYC",
        "freebase": "LINKED_DATA_FREEBASE",
        "ciaFactbook": "LINKED_DATA_FACTBOOK",
        "census": "LINKED_DATA_CENSUS",
        "geonames": "LINKED_DATA_GEONAMES",
        "musicBrainz": "LINKED_DATA_MUSICBRAINZ",
        "crunchbase": "CRUNCHBASE_WEB_LINK",
   "entities": [ 
        "entity": {
            "type": "DETECTED_TYPE",
            "relevance": "DETECTED_RELEVANCE",
            "count": "DETECTED_COUNT",
            "text": "DETECTED_ENTITY",
            "sentiment": {
                "type": "SENTIMENT_LABEL",
                "score": "SENTIMENT_SCORE",
                "mixed": "SENTIMENT_MIXED"
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