When any extraordinary event takes place in the world wide area, it is the social media that acts as the fastest carrier of the news along with the consequences dealt with that event. One can gather much information through social networks regarding the sentiments, behavior, and opinions of the people. In this paper, we focus mainly on sentiment analysis of twitter data of India which comprises of COVID-19 tweets. We show how Twitter data has been extracted and then run sentimental analysis queries on it. This is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and are either positive or negative or neutral in some cases.

Systems powered by artificial intelligence are being developed to be more user-friendly by communicating with users in a progressively human-like conversational way. Chatbots, also known as dialogue systems, interactive conversational agents, or virtual agents are an example of such systems used in a wide variety of applications ranging from customer support in the business domain to companionship in the healthcare sector. It is becoming increasingly important to develop chatbots that can best respond to the personalized needs of their users so that they can be as helpful to the user as possible in a real human way. This paper investigates and compares three popular existing chatbots API offerings and then propose and develop a voice interactive and multilingual chatbot that can effectively respond to users mood, tone, and language using IBM Watson Assistant, Tone Analyzer, and Language Translator. The chatbot was evaluated using a use case that was targeted at responding to users needs regarding exam stress based on university students survey data generated using Google Forms. The results of measuring the chatbot effectiveness at analyzing responses regarding exam stress indicate that the chatbot responding appropriately to the user queries regarding how they are feeling about exams 76.5%. The chatbot could also be adapted for use in other application areas such as student info-centers, government kiosks, and mental health support systems.

CIMON (The Crew Interactive Mobile Companion 2) has been busy working with astronauts aboard the International Space Station. The robotic assistant is now using a tone analyzer, detecting emotions during the current voyage. CIMON made its debut on the ISS in November of this year, with Space.com's "During the experiment, CIMON successfully found and recognized Gerst's face, took photos and video, positioned itself autonomously within the Columbus module using its ultrasonic sensors, and issued instructions for Gerst to perform a student-designed experiment with crystals. Weighing about 5 kilograms (11 lbs. on Earth), the 3D-printed robot designed jointly by the German space agency DLR, Airbus, and IBM works similarly to Apple's virtual assistant Siri or Amazon's Alexa. "If CIMON is asked a question or addressed, the Watson AI firstly converts this audio signal into text, which is understood, or interpreted, by the AI," explained IBM project lead Matthias Biniok in the statement. "IBM Watson not only understands content in context, [but] it can also understand the intention behind it." There is a great video here, of Gerst conversing with CIMON, and it shows the complexity of this fantastic technology. Especially regarding the amount of relevant information that it can store and relay to astronauts, making their jobs easier. The Watson team at IBM computing only added the tone analyzer to the standard set of Watson capabilities this week. However, CIMON 2 was added as a seventh crew member on SpaceX Dragon during a resupply mission last week. In addition to updated software, the robot also got a hardware upgrade, with enhanced sensitivity on its microphones, and a more advanced sense of orientation. The German Aerospace Center and Airbus are the other crew members for this CIMON project. "IBM is using its tone analyzer technology to analyze how CIMON converses with the astronauts.

The next iteration of a floating, artificially intelligent astronaut assistant is on its way to the International Space Station (ISS). Developed by Airbus in partnership with IBM and the German Aerospace Center, CIMON 2 is the latest model of the robot CIMON, short for Crew Interactive Mobile Companion. The first iteration, CIMON 1, weighed eleven pounds and served as a free-floating AI assistant for astronauts as they completed mission duties in space. The assistant was able to understand what the astronauts were saying in context, as well as the intention behind it. Like tabletop assistants, CIMON 1 could recognize speech and speak with a synthetic voice.

Tech service provider NTT released a report on Monday outlining the top digital disruption predictions for 2020. In the report, NTT CTO Ettienne Reinecke highlighted five specific disruptive technologies expected to impact 2020. After gathering global insights on intelligent tech solutions from clients, NTT experts determined the future's most impactful disruptive technologies. Gartner's IT glossary defines digital disruption as "an effect that changes the fundamental expectations and behaviors in a culture, market, industry or process that is caused by, or expressed through, digital capabilities, channels or assets." SEE: Digital transformation: An IT pro's guide (free PDF) (TechRepublic) While the word disruption may have a negative connotation, digital disruption is a positive movement for the tech world.

IBM Watson and Daybreaker hosted the World's First Cognitive Dance Party in San Francisco by using Watson Tone Analyzer, Watson Personality Insights, Chef Watson and Watson Beat. With Personality Insights API Daybreak was able to base the colors, music playlists, kick-off fitness session, healthy breakfast, and intention card all on the each attendees' personality. Tone Analyzer drove the color of a rising cognitive sun based on sentiment analysis of tweets of around the country. While Watson Beat created new riffs using inputs from pianist ELEW, using one or several of his musical filters. Even the Breakfast was courtesy of Chef Watson, which featured unexpected ingredient combinations, tailored again to attendee personality.

IBM is blazing a trail when it comes to how robots interact with humans. The company recently upgraded its Watson AI with a'Tone Analyzer' service that allows it to understand nuance and tone of text. By analyzing word choice, emoji use, and context the AI is learning to understand how we talk to each other. There are thousands of companies doing work in the AI space right now, but most of it happens in the background. It's one thing to teach an AI how to find what you're looking for on the internet, it's more impressive to give AI manners and train it to be a customer service agent.

IBM is blazing a trail when it comes to how robots interact with humans. The company recently upgraded its Watson AI with a'Tone Analyzer' service that allows it to understand nuance and tone of text. By analyzing word choice, emoji use, and context the AI is learning to understand how we talk to each other. There are thousands of companies doing work in the AI space right now, but most of it happens in the background. It's one thing to teach an AI how to find what you're looking for on the internet, it's more impressive to give AI manners and train it to be a customer service agent.

We are pleased to announce the launch of a new Tone Analyzer endpoint trained for Customer Engagement scenarios. The new endpoint was trained on customer support conversations on twitter, and the tones included are frustrated, sad, satisfied, excited, polite, impolite and sympathetic. Currently, the new endpoint is Beta functionality in the IBM Watson Tone Analyzer service. Given a textual conversation between a customer and an agent or company representative, the service detects the above mentioned tones both from the customer's and the agent's text. Why Did We Build the Tone Analyzer for Customer Engagement Endpoint?

IBM's Watson artificial intelligence platform is getting a new enhancement that will help the system detect human emotion in customer service situations. According to a blog post published Thursday, IBM Watson distinguished engineer and master inventor Rama Akkiraju said the new Tone Analyzer for Customer Engagement tool is designed to help customer service agents and chatbots craft appropriate responses to frustrated, sad, or satisfied customers. Through linguistic analysis, the tool can pick up on seven different types of tone via conversations with customer service agents and chatbots: frustration, satisfaction, excitement, politeness, impoliteness, sadness and sympathy. The system also claims to be able to detect these sentiments in emojis, emoticons, and slang. The Tone Analyzer was developed with a machine learning algorithm that trained on customer support conversations on Twitter.