Udemy Course Text Mining and Sentiment Analysis with Tableau and R NED Text Analysis 101: Sentiment Analysis in Tableau & R. At the Tableau Partner Summit in London I attended a session about statistics and sets in Tableau. In this session, Oliver Linder, Sales Consultant at Tableau Bestseller What you'll learn Connect Twitter and R to harvest Tweets for certain keywords Perform sentiment analysis based on a simple lexicon approach Clean and process Tweets for further analysis Export text based data and sentiment scores from R Use Tableau to visualize sentiment analysis data Identify situations where sentiment analysis can be applied in a company Description Extract valuable info out of Twitter for marketing, finance, academic or professional research and much more. This course harnesses the upside of R and Tableau to do sentiment analysis on Twitter data. With sentiment analysis you find out if the crowd has a rather positive or negative opinion towards a given search term. This search term can be a product (like in the course) but it can also be a person, region, company or basically anything as long as it is mentioned regularly on Twitter.

Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models. In particular, representations encoded in models often inadvertently perpetuate undesirable social biases from the data on which they are trained. In this paper, we present evidence of such undesirable biases towards mentions of disability in two different English language models: toxicity prediction and sentiment analysis. Next, we demonstrate that the neural embeddings that are the critical first step in most NLP pipelines similarly contain undesirable biases towards mentions of disability. We end by highlighting topical biases in the discourse about disability which may contribute to the observed model biases; for instance, gun violence, homelessness, and drug addiction are over-represented in texts discussing mental illness.

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses. Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain.

But how do you turn that feedback into meaningful customer insights? In the past, companies used things like surveys to try to narrow down a general good/bad/neutral response to their recent marketing campaign or product. Still, there is so much more information in the form of unstructured data that could help companies better understand their customers. Whether they are using social media, blogs, forums, reviews, or online news commenting, customers are sharing their opinions in tons of different ways every single day. The only issue: many of these opinions are shared in nuanced ways that traditional AI hasn't been able to navigate.

Video conference app Zoom illegally shared personal data with Facebook, even if users did not have a Facebook account, a lawsuit claims. The app has experienced a surge in popularity as millions of people around the world are forced to work from home as part of coronavirus containment measures. The lawsuit, which was filed in a California federal court on Monday, states that the company failed to inform users that their data was being sent to Facebook "and possibly other third parties". It states: "Had Zoom informed its users that it would use inadequate security measures and permit unauthorised third-party tracking of their personal information, users... would not have been willing to use the Zoom App." The allegations come amid a flurry of questions surrounding Zoom's privacy policies, with the Electronic Frontier Foundation recently warning that the app allows administrators to track the activities of attendees.

Narratives are an important human tool for communication, representation and understanding. Natural Language Processing already offers many instruments that enable the automatic extraction of narrative elements from texts, including Named Entity Recognition, Semantic Role Labeling, Sentiment Analysis, Anaphora Resolution, Temporal Reasoning, etc. The storyfication of data is being used to generate textual reports on finance and sports, among others. Timelines and infographics can be employed to represent in a more compact way automatically identified narrative chains in a large set of news articles, assisting human readers in grasping complex stories with different moments and a network of characters. While the Automatic Generation of Text shows impressive results towards computational creativity, it still needs to develop means for controlling the narrative intent of the output.

In the Finance Industry, Alternative Data is used to give investors an information advantage. Quantitative Hedge Funds have used trading models based on Alternative Data for many years. The most common Alternative Data signal used in quantitative trading and quantitative investing is based on text data from the Internet, and the trading models can broadly be defined as algorithmic trading models and as statistical arbitrage models. It has been suggested that text analysis is the key to success for the most successful money manager of all times. The trading model can use text data and sentiment data as the only, or as one of several, inputs, and it can be the main strategy, or one of several strategies, in a hedge fund. Some traditional funds use text-based signals to build the models they use as an overlay to other strategies and as a risk indicator for tactical asset allocation.

The CB Insights 4th annual AI 100 finalists include AI startups from 13 countries, pushing the boundaries of AI research and commercial adoption across 15 industries and a broad range of cross-industry applications. The CB Insights research team picked the 100 companies from nearly 5K startups, based on several factors including patent activity, business relations, investor profile, news sentiment analysis, proprietary Mosaic scores, market potential, competitive landscape, team strength, and tech novelty. Startups are categorized by their main focus areas. Categories in the market map below are not mutually exclusive.

This article explores some new and emerging applications of text analytics and NLP in healthcare. Each application demonstrates how HCPs and others use natural language processing to mine unstructured text-based healthcare data and then do something with the results. Healthcare databases are growing exponentially, and text analytics and natural language processing (NLP) systems turn this data into value. Healthcare providers, pharmaceutical companies and biotechnology firms all use text analytics and NLP to improve patient outcomes, streamline operations, and manage regulatory compliance. In fact, 26 million people have already added their genetic information to commercial databases through take-home kits.

Researchers say evaluating the'social-connectedness' of regions using Facebook data could give epidemiologists another tool in judging the spread of infectious disease outside of geographic proximity and population density. The study, which appears in the preprint journal ArXiv and is authored by researchers from New York University, found links between two hotspots of the ongoing COVID-19 pandemic - Westchester County, New York and Lodi province in Italy - to areas with correlating connections on the social media platform, Facebook. Using an equation developed by the same researchers in 2017 called the'Social Connectedness Index' the study was able to make correlations between the spread of COVID-19 from Westchester County and Lodi to geographically disparate locations like ski resorts on Florida and vacation spots in Rimini, Italy near the Adriatic sea. Those correlations remained even after controlling for wealth, population density, and geographic proximity according to researchers. Levels of social connectedness didn't always correlate to the disproportionate spread of the virus, however.