Webtunix AI

www.webtunix.com

Webtunix AI is an emerging technology company that provides machine learning and data science services to businesses using publically available data to the web. With Machine Learning, Webtunix analyzes user behaviour and builds relevancy models that learn and improve as data, content and user activity grows and evolves. According to the client’s perspective, these technologies help in two ways: by understanding the Intent behind the query itself, based on content and by delivering highly relevant results from search queries. Machine Learning features Include: 1.Sentiment analysis and automated classification of unstructured content. 2.Behavioural analytics from frequency, past actions or actions of similar users. 3.Comparisons of Unstructured Content (titles, descriptions, article leads etc.) for downstream analysis or end-user applications. 4.Recommendations: Contextual information based on preferences, user behaviours and content similarities 5.Relationships between content items based on metadata, topics, concepts, genres or entities (such as names of people, organizations and locations)

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Webtunix AI is an emerging technology company that provides machine learning and data science services to businesses using publically available data to the web. With Machine Learning, Webtunix analyzes user behaviour and builds relevancy models that learn and improve as data, content and user activity grows and evolves. According to the client’s perspective, these technologies help in two ways: by understanding the Intent behind the query itself, based on content and by delivering highly relevant results from search queries. Machine Learning features Include: 1.Sentiment analysis and automated classification of unstructured content. 2.Behavioural analytics from frequency, past actions or actions of similar users. 3.Comparisons of Unstructured Content (titles, descriptions, article leads etc.) for downstream analysis or end-user applications. 4.Recommendations: Contextual information based on preferences, user behaviours and content similarities 5.Relationships between content items based on metadata, topics, concepts, genres or entities (such as names of people, organizations and locations)

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2015

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  • Chief Executive Officer

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    Phone (***) ****-****
  • Content Writer

    Email ****** @****.com
    Phone (***) ****-****

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