Elon Musk has gained access to the Twitter data that he said was needed to complete his $44 billion acquisition, but data scientists and specialists doubt the stream will provide the conclusive answers he seeks about the number of phony accounts on the platform. After some legal back-and-forth between the two sides, Twitter in recent weeks provided Mr. Musk with historical tweet data and access to its so-called fire hose of tweets, people familiar with the matter said. That fire hose shows the full flood of all tweets--people post hundreds of millions of times a day on the platform, according to the company--in near real time.
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. "When captured electronically, customer sentiment -- expressions beyond facts, that convey mood, opinion, and emotion -- carries immense… It's free, we don't spam, and we never share your email address.
If the question'What is sentiment analysis?' popped up in your mind as you clicked on this blog, I think you will find my first blog in this series interesting. Essentially, sentiment analysis is a natural language processing technique used to determine the emotional tone of textual data. It is primarily used to understand customer satisfaction, and gauge brand reputation, call center interactions as well as customer feedback and messages. There are various types of sentiment analysis that are common in the real world. In this part of my blog series, let me walk you through the implementation of sentiment analysis.
This book is intended to support data scientists and developers so they can quickly enter the area of text analytics and natural language processing. Thus, we put the focus on developing practical solutions that can serve as blueprints in your daily business. A blueprint, in our definition, is a best-practice solution for a common problem. It is a template that you can easily copy and adapt for reuse. For these blueprints we use production-ready Python frameworks for data analysis, natural language processing, and machine learning.
We are now privy to a spectacular array of communication tools with the potential to connect us all for greater understanding and tolerance. But SA software is counterproductive for open dialog at best, and fundamentally corrosive at worst. It is sure to infuse discord and distrust, much the way the internet is now viewed--isolating us, dividing us into segmented groups--when, at the net's inception, it was supposed to unite the planet. Even more destructive, SA software for SaaS products, like spying on our kids, or when used for marketing to influence elections by insighting ignorant, angry people to elect the second-coming of Hitler, has, and will continue to put even greater distance between us.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Over the past two weeks, emotions have run high around the evolution and use of emotion artificial intelligence (AI), which includes technologies such as voice-based emotion analysis and computer vision-based facial expression detection. Video conferencing platform Zoom came under fire after saying it might soon include emotion AI features in its sales-targeted products. A nonprofit advocacy group, Fight for the Future, published an open letter to the company: It said Zoom's possible offering would be a "major breach of user trust," is "inherently biased," and "a marketing gimmick." Meanwhile, Intel and Classroom Technologies are working on tools that use AI to detect the mood of children in virtual classrooms.
Every day a large number of social media users are produced who can be used to analyze their ideas on any event, film, product or politics. Common tools like Apache Storm analyze streams in micro-batch while novel tools like Apache Spark process data in real time to make analyzing and processing real-time data possible.
The function for the negation handler is available at my Github repo. An example of the function output is shown below. 'Negation' is the main function being called on the tokenized sentence as shown. In the function, whenever a negation word (like'not', "n't", 'non-', 'un-', etc) is encountered, a set of cognitive synonyms called synsets are generated for the word next to the negation. These synsets are interlinked by conceptual semantic and lexical relations to each other in a lexical database called WordNet.