In the current fast developing Machine Learning climate, Computers can be very accurately trained to see and understand human sentiment and moods. Optisol has provided human-based Machine Learning to our customers in various fields such as Industrial, Healthcare, Law etc. These solutions detect human activities, gestures, emotions and sentiment.
To highlight one such exemplar, we developed a robust subscription-based web application that recognizes visitor’s faces, age, gender and detects the mood of a person that walks into any retail store. The customer wanted to improve the in store closure rate from 32% and had identified that by being able to read potential customers’ moods when they were in the store would allow for greater success and therefore increased revenue. The solution starts with cameras at the point of capture (in-store). This video feed is streamed to Kinesis Video Stream, then subsequently processed in Microservices Architecture.
The implementation of machine learning on the live-streamed video took a total of 3 months to complete and provided the store service staff with great insight to each potential shopper. In combination with appropriate training, the store staff are able to approach shoppers with specific narratives based on their estimated age and emotional states, this resulted in the closure rate rising to 59%.
With the assistance of insights generated, the store owner was able to gauge the overall sentiment of customers and was then able to incrementally bring changes to the store. They measured the effectiveness of the changes (on new overall sentiment). This process of incremental change helped them transform their store with the help of real-time insights from shoppers in a very effective way. Optisol is using the latest advances and cutting-edge technologies in Vision Intelligence to help other clients in this same way.
The idea of this project is to build a sentiment analysis model that detects the emotions that underlie a tweet. It makes associations between words and emotions and the aim is to classify the tweets into sentiments like anger…
Cognitive Computing technology is used to perform specific tasks to facilitate human intelligence. Cognitive AI analyses a huge amount of data to offer insights, suggestions, and even automated actions.