All stories

Deep Talk is a great platform to analyze the content on Twitter.

Natural Language Processing (NLP) is a discipline with a long history. It was born in the 1950s as a sub-area of Artificial Intelligence and Linguistics, with the aim of studying the problems derived from the automatic generation and understanding of natural language. Although works from earlier periods can be found, it was in 1950 that Alan Turing published an article entitled “Intelligence” that proposed what is now called the Turing test as a criterion of intelligence (see Turing test).

Companies that can use unstructured data as a relevant asset in their decisions have an advantage over those that only try to access 20% of the total available data.

The use of deep learning technologies in massive citizen consultation processes allows to quickly and efficiently process thousands of proposals in order to give viability to this type of democratic exercise.

In this report we built a system to detect urgent, high-risk messages in a chat application connecting its users to psychologists. With a group of volunteering psychologists, we categorised segment pairs of conversations into high-risk messages that require urgent attention and normal messages. We annotated 1795 examples out of which 612 are emergencies. We were thus facing an imbalanced classification problem.

Supervised and unsupervised deep learning models are two different ways of analyzing text data.

No-Code AI platforms are the future and Deep Talk is the leading platform for text analysis.

Getting the data from Twitter can be complicated sometimes. But with some tools, the process can be easy.

The analysis of real conversations between citizens and government or organizations that serve people in the areas of health, education, security, etc. through the application of deep learning models such as those used by Deep Talk, is a powerful tool for public policy by extracting relevant data for decision making directly from the conversation of a citizen and not through indirect methods.

The term Low-Code was first used in 2014 by analysts at Forrester. The report published that year mentioned that Low-code platforms with a graphical interface and drag and drop elements would dramatically reduce the amount of hand-coding needed to launch a project. LCNC technology is derived from older RAD (Rapid Application Development) technologies, such as Lotus Notes or Microsoft Access, which also allowed non-IT teams to do some of the development, but still required in-depth knowledge.

Written conversations with customers are a huge source of data that is often not used due to the complexity of their processing. Deep Talk allows companies, customer success, customer experience, and AI teams to transform this unstructured data into valuable data for decision-making.

Classification models using deep learning can speed up the process to create chat bots.

Companies can predict customer satisfaction and NPS using Artificial Intelligence.

7 best survey analysis tools for 2022. Some platforms are very traditional and with many years in the market, and others like Deep Talk are disrupting the market using AI techniques like machine learning and deep learning to analyze the data.