Machine learning is used for generating information and knowledge via a technical system, e.g., a software. The system learns patterns or structures using previous examples and is subsequently able to independently evaluate and classify information and data. Sentiment analysis, content monitoring, technology categorization, predictive coding, clustering, alerting, and documents search. Machine learning has already become very important in the context of big data since it enables processing large amounts of data quickly and easily.

In this video I will introduce you to the GRAPH API, I will use the GRAPH API Explorer and show you some example requests.

The global text analytics market size is estimated to grow from $3.97 billion in 2017 to $8.79 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 17.2%. The customer experience management (CEM) is expected to hold the largest market share during the forecast period. Among the various applications in the text analytics market, the CEM application is expected to hold the largest market share during the forecast period. Text mining is the most traditional application in customer service and is frequently utilized to improve customer experience through various information sources. Today, text analytics is implemented to offer quick, computerized feedback to the clients, which significantly reduces dependency on executives for resolving issues.

For a more technical explanation, this and this article can be read. Here you can find a good explanation as well as a list of the mostly used Kernel functions.

Have you ever wondered how Watson, IBM's AI works? Lastly there's empathy where Watson has tone analysis, emotion analysis and can provide personality insights. Watson's tone analyzer for example uses psycholinguistics, emotion analysis and language analysis to assess tone. Now Expressive SSML and Voice Transformation SSML bring life and a human lilt to computed voices.

However, most importantly, the restaurant owner has the most scope for extracting valuable snippets of insights from customer reviews with ratings between 3-4/5. I recently had a chance to deliver a talk in a conference titled'Understanding Consumers in the Digital World', held at IIM Lucknow, Noida Campus on 16-17th November 2015. The audience mainly comprised of marketers, market research professionals and academics whose work is primarily focused on obtaining deep insights by understanding the online consumers. My talk was titled'Decoding Ratings for superior service in restaurants – Using text to understand customers'. The focus was quite simple – convince and demonstrate how to read and understand customers from their reviews, not ratings. Our product, Lunchbox, a complete restaurant management solution was showcased as well.

Using code KDNUGGETS to save - early reg by May 31.

Organizations have generated unprecedented amounts of employee feedback through weekly or monthly pulse surveys, annual engagement surveys, and internal social networks and collaboration platforms. But many still struggle with how to efficiently comb through that mountain of information to identify actionable insights leaders can use to improve employee engagement and retention. Some companies are now turning to artificial intelligence (AI) tools to conduct sentiment analysis on employee feedback, gauge how employees feel and address their concerns. While text analysis of survey responses isn't new, the emergence of smarter algorithms enables faster and more precise search and categorization of unstructured data, such as open-ended comments, said Alan Lepofsky, vice president and principal analyst with Constellation Research, a technology research firm in Silicon Valley. Lepofsky, author of the recent report Why Artificial Intelligence Will Power the Future of Work, said vendors have made advances in sentiment analysis technology.

MeaningCloud, leader in SaaS semantic analytics, has new RapidMiner extension that offers very powerful and flexible text analytics and the ability to extract the meaning of any unstructured text.

With Firebase and Cloud Functions, you can easily add machine learning to your app to do translation, sentiment analysis, speech recognition, and computer vision. See all the talks from Google I/O '17 here: https://goo.gl/D0D4VE