Revolutionize your news monitoring strategy with natural language processing tasks. Learn to navigate the digital landscape effectively by categorizing negative articles for employee alerts and curating positive ones into a press book. Elevate your approach to news management and stay ahead in today’s dynamic business environment.
You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book. Which natural language processing tasks should you use to complete the process? (To answer, drag the appropriate tasks to the correct locations. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.)
Box 1: Entity recognition. The Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text. Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:
- Which companies were mentioned in a news article?
- Does a tweet contain the name of a person? Does the tweet also provide his current location?
- Were specified products mentioned in complaints or reviews?
Box 2: Sentiment Analysis. The Text Analytics API’s Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as “negative”, “neutral” and “positive”) and confidence scores at the sentence and document-level.
Microsoft Docs > Previous Versions > Module Categories and Descriptions > Text Analytics > Named Entity Recognition
Microsoft Docs > Azure > Cognitive Services > What is sentiment analysis and opinion mining in Azure Cognitive Service for Language?
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