Twitter Sentiment Analysis in 10 Minutes Using Machine Learning

Feb 27, 20201 like58 views

About the webinar Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook. This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue! Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more. What you'll learn - How businesses are leveraging sentiment analysis to their advantage - Best practice to automate machine learning models in hours not months - Demo: How to build a twitter sentiment analysis model

Twitter Sentiment Analysis
in 10 minutes with
Machine Learning

Technology leader with 20+ years expertise in Product Development, Business strategy and
Artificial Intelligence acceleration. Active contributor in the New York AI community
Extensively worked with global organizations in BFSI, Healthcare, Insurance, Manufacturing,
Retail and Ecommerce to define and implement AI strategies
Nisha Shoukath
Co-founder, People10 & Skyl.ai
The Speaker

Shruti Tanwar
Lead - Data Science
Extensive experience building future tech products using Machine Learning and
Artificial Intelligence.
Areas of expertise includes Deep Learning, Data Analysis, full stack development
and building world class products in ecommerce, travel and healthcare sector.
The Speaker

CTO & Software Architect with 15 years of experience working at the
forefront of cutting-edge technology leading innovative projects
Areas of expertise include Architecture design, rapid product
development, Deep Learning and Data Analysis
The Panelist
Bikash Sharma
CTO and Co-founder at Skyl.ai

All dial-in participants will be muted to enable the
presenters to speak without interruption
Getting familiar with ‘Zoom’
Questions can be submitted via Zoom Questions chat
window and will be addressed at the end during Q&A
The recording will be emailed to you after the webinar
Please familiarize yourself with the Zoom ‘Control Panel’ on your screen

A quick intro about Skyl.ai
ML automation platform for unstructured data
Guided Machine Learning
Workflow
Build & deploy ML models
faster on unstructured data
Collaborative Data Collection
& Labelling
Easy-to-use & scalable AI
SaaS platform

Demo: Twitter sentiment
analysis in 10 minutes
...In the next 45 minutes
Diving into
Sentiment Analysis
1 2

POLL #1
At what stage of Machine learning adoption your
organization is at?
⊚ Exploring - Curious about it
⊚ Planning - Creating AI/ML strategy
⊚ Experimenting - Building proof of concepts
⊚ Scaling up - Some departments are using it
⊚ In production - Using it in product features
⊚ Transforming - AI/Ml driven business

Opinion Mining i.e. advanced text mining techniques to analyze the
sentiment of the text in the form of positive, negative and neutral
Sentiment = Opinion = Emotions = Attitude
Sentiment Analysis

Why do we need it?
Traditional BI tools cannot
⊚ Capture sarcasm or learn new slangs
⊚ Extract insights from large volume of unstructured data
⊚ Help brands gauge reactions of customers

Donald Trump's tweet activity pattern from 2009 onwards
Source: Twitter Usage

Applications of sentiment Analysis
Marketing
Social media
monitoring
Ecommerce
Product/Service
Reviews
Politics
Election results,
perception of
policies
Public Actions
social phenomena,
red alert situations

POLL #2
Some challenges that you are facing while
implementing AI & Machine Learning
⊚ Not started yet, so no
challenges
⊚ Data collection
⊚ Data Labeling
⊚ Large volumes of data
⊚ Identifying the right data set to
train
⊚ Lack of knowledge of ML tools
⊚ Lack of end to end platform
⊚ Lack of expertise
⊚ Choosing the right
algorithms

Demo: Twitter Sentiment
Analysis in 10 minutes0
2

Try out 15 days free trial with complimentary
consultation on pilot project
Register https://skyl.ai/form?p=start-trial

Questions?
contact@skyl.ai
https://skyl.ai/
?

85 Broad Street, New York, NY, 10004
+1 718 300 2104, +1 646 202 9343
contact@skyl.ai
We hope to hear from you soon
Thank you for joining!

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How an AI-backed recommendation system can help increase revenue for your onl...Skyl.ai

This document discusses how AI-backed recommendation systems can increase revenue for online stores. It introduces the speakers and provides an agenda for the webinar. The webinar will include a live demo of a product recommendation system using machine learning. It will discuss how e-commerce giants use these systems to drive sales and improve customer experience. It will also cover how to quickly overcome challenges in building ML models. Attendees will have a chance to ask questions during a Q&A session.

Future of Ecommerce: How to Improve the Online Shopping Experience Using Mach...Skyl.ai

About the webinar It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous, and fragmented data. This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product. In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly. What you will learn - How E-commerce companies are using AI to drive more sales and seamless customer experience - Know the secret sauce of automating time-intensive, repetitive steps to quickly build models - Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai

test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...Skyl.ai

About the webinar It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous and fragmented data. This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product. In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly. What you'll learn - How E-commerce companies are using AI to drive more sales and seamless customer experience - Know the secret sauce of automating time-intensive, repetitive steps to quickly build models - Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai

AI in Healthcare: How to Implement Medical Imaging Using Machine Learning?Skyl.ai

About the webinar According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused. The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions. Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly. What you will learn: - How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging - Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months - Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai

AI in Healthcare: Can AI Help in Diagnosing CoronavirusSkyl.ai

About the webinar The entity that has caused a newfound global love of hand sanitizers and masks? The Coronavirus (known as ‘2019-nCov’ or ‘Covid-19), which has infected about 5,00,000 people globally within a few months! According to the WHO: 'In the most severe cases, the infection can cause pneumonia, severe acute respiratory syndrome, and even death.' Statements like these beg the question: 'How accurate are the tests to spot the disease?' 'Can AI assist in giving a more accurate diagnosis?' The AI Model generated via Skyl.ai’s deep learning platform can accurately detect COVID-19 through patterns in X-ray scans and differentiate it from community-acquired pneumonia and other lung diseases that may otherwise be overlooked by a doctor. Through this webinar, we will demo how AI can be used to test the Covid19 infections, and help in treating patients with critical conditions quickly. What you'll learn - How healthcare institutions can leverage AI to detect COVID-19 and reduce the time taken to provide critical care to patients who are affected. - Discuss the approach to automate the machine learning workflow, creating and deploying models in hours and not weeks or months. - Demo: How to create an ML model that can detect COVID-19 from chest x-rays using Skyl.ai.

AI in Insurance: How to Automate Insurance Claim Processing with Machine Lear...Skyl.ai

About the webinar Insurance companies are looking at technology to solve complexity created by presence of cumbersome processes and presence of multiple entities like actuaries, support team and customers in the claim processing cycle. Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle. In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI. What you'll learn - How Insurance companies are using ML to drive more efficiency and business gain - Best practices to automate machine learning models - Demo: A deeper understanding of the end-to-end machine learning workflow for car damage recognition using Skyl.ai

How AI is Changing Medical Imaging in the Healthcare Industry Skyl.ai

About the webinar According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused. The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care and interconnected health conditions. Through this webinar, we will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly. What you'll learn - How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging - Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months - Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai

How to Implement Biomedical Named Entity Recognition with Machine Learning Skyl.ai

Biomedical research & healthcare practices are generating information like scientific publications, transcription and EMR records in an unprecedented way. For example, the new generation of sequencing tech is helping to process billions of DNA sequence data per day. Further, Biomedical vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively tag, index and manage this fast and ever-growing knowledge. Through this webinar, we will understand how Machine Learning can be used to automate complex processes and help in extracting key entities like the chemicals, diseases, genes, proteins, anatomical constituents, organization name, etc. What you'll learn - How organizations are leveraging Machine Learning in biomedical & healthcare industry - Best practice to automate machine learning models in hours not months - Live demo - Identify & classify complex medical terms & names with NERC