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Microsoft adds Hindi to strengthen Azure Text Analytics service

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New Delhi — said that it has added Hindi as the latest language under its service. A part of the Cognitive Services, Text Analytics helps the company's customers in to find out what people think of their brand or topic.

The functionality provided by Text Analytics includes sentiment analysis, opinion mining, key phrase extraction, language detection, named entity recognition, and PII detection. With the addition of Hindi, now sentiment analysis supports more than 20 languages, said the company.

Along with Hindi, the other prominent languages supported include English, French, Italian, German, Spanish, Portuguese, Dutch, Swedish, Norwegian (Bokmål), Danish, Finnish, Polish, Greek, Russian, Chinese (traditional), Chinese (simplified), Japanese, Korean, and Turkish.

“Underlining our commitment to helping empower every business to achieve more, Microsoft has added Hindi to the already robust set of international languages supported by Text Analytics service,” said Sundar Srinivasan – General Manager – AI & Search – Microsoft India.

Also Read | Microsoft introduces Tamil 99 keyboard on Windows 10

“We are helping brands break language barriers and reach out to Hindi-speaking customers to understand the customer's sentiment about their products, services, and broaden their user feedback reach. With this release, we are bringing in cutting edge cloud services, AI, and to deepen the trust between brands and customers in India, he added.

According to Chris Wendt, Program Manager, Azure Language services said that Sentiment Analysis is the primary feature of Text Analytics, because it gives broad insight into the perception of product and organization, delivering a fast and actionable signal whether things are going well, or require corrective action.

The Sentiment Analysis feature provides more granular information about the opinions related to aspects (such as the attributes of products or services from a brand) in text. Businesses can extract insights from customer service calls by using Speech-to-Text, Sentiment Analysis, and Key Phrase Extraction in a single workflow. These results can be displayed in a Power BI dashboard or a portal to better understand customers, highlight customer service trends, and drive customer engagement.

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