Machine learning, basically a set of code built on repetitively analysing huge blocks of data sets, is being leveraged to predict the base sentiment or mood of the user in order to draft an appropriate response.
copyright by economictimes.indiatimes.com
AI startups have also developed suites out of chat bot products, such as enterprise-grade conversational AI platforms sold to larger companies. A gamut of human expression, from joyous whoops to irate snarls, is now fodder for chat bots. Powered by Artificial Intelligence, these chat bots are detecting consumer sentiment and tone using their platforms and building a repository of oft-used exclamations to mark out an angry or pleased customer. This has become particularly important in a digital age where brands slipping in customer service standards stare at severe censure in social media. A chat bot breaks down a user’s sentence into simpler components, sometimes in very basic English to pick out the topic in reference, intent and sentiment – as an added layer of analytics.
Machine learning, basically a set of code built on repetitively analysing huge blocks of data sets, is being leveraged to predict the base sentiment or mood of the user in order to draft an appropriate response.
“A dependency parser (a software that enables computers to process natural language) analyzes the grammatical structure of a sentence, establishing relationships between “head” words and words which modify those heads,” said Damodharan Padmanabhan, CEO of AI startup PositiveNaick Analytics.
His company has built a sentiment lexicon over two years, enabling its machine learning software to pick out even informal symbols of human sentiment like emojis and smileys.
“Every word keyed in is an input for us to analyse,” says Padmanabhan.
Interestingly, programming and language proficiency, both English and regional, are put together to develop chat bots, spawning demand for “soft skills” in the technology domain. Language proficiency ranks high in priority during job interviews, he says.
AI startups have also developed suites out of chat bot products, such as enterprise-grade conversational AI platforms sold to larger companies.
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Kore.ai, a Florida-based company founded by serial entrepreneur Raj Koneru with product developments teams in Hyderabad, offers a platform feature that methodically analyses sentiment by adding machine learning strengths to programs that detect human emotions.
“As part of our Natural Language Processing approach, we evaluate user inputs to find these six possible emotions — anger, disgust, fear, sad, joy and positivity,” said Sairam Vedam, chief marketing officer for Kore.ai. Machine learning helps the bot learn through iterations, besides additional training using synonyms, patterns and whole utterances, which reduce the amount of manual training inputs by the developer and therefore the time to get a chat bot up and running, Vedam said. Sarcasm is one tricky situation where a bot might trip up, plunging the machine into confusion about the intent of the user. Manoj Malhotra, co-founder and CTO at amplify.ai, says efforts are on to detect sarcasm, too.[…]
read more – copyright by economictimes.indiatimes.com
Machine learning, basically a set of code built on repetitively analysing huge blocks of data sets, is being leveraged to predict the base sentiment or mood of the user in order to draft an appropriate response.
copyright by economictimes.indiatimes.com
Machine learning, basically a set of code built on repetitively analysing huge blocks of data sets, is being leveraged to predict the base sentiment or mood of the user in order to draft an appropriate response.
“A dependency parser (a software that enables computers to process natural language) analyzes the grammatical structure of a sentence, establishing relationships between “head” words and words which modify those heads,” said Damodharan Padmanabhan, CEO of AI startup PositiveNaick Analytics.
His company has built a sentiment lexicon over two years, enabling its machine learning software to pick out even informal symbols of human sentiment like emojis and smileys.
“Every word keyed in is an input for us to analyse,” says Padmanabhan.
Interestingly, programming and language proficiency, both English and regional, are put together to develop chat bots, spawning demand for “soft skills” in the technology domain. Language proficiency ranks high in priority during job interviews, he says.
AI startups have also developed suites out of chat bot products, such as enterprise-grade conversational AI platforms sold to larger companies.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Kore.ai, a Florida-based company founded by serial entrepreneur Raj Koneru with product developments teams in Hyderabad, offers a platform feature that methodically analyses sentiment by adding machine learning strengths to programs that detect human emotions.
“As part of our Natural Language Processing approach, we evaluate user inputs to find these six possible emotions — anger, disgust, fear, sad, joy and positivity,” said Sairam Vedam, chief marketing officer for Kore.ai. Machine learning helps the bot learn through iterations, besides additional training using synonyms, patterns and whole utterances, which reduce the amount of manual training inputs by the developer and therefore the time to get a chat bot up and running, Vedam said. Sarcasm is one tricky situation where a bot might trip up, plunging the machine into confusion about the intent of the user. Manoj Malhotra, co-founder and CTO at amplify.ai, says efforts are on to detect sarcasm, too.[…]
read more – copyright by economictimes.indiatimes.com
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