sentiment


Redtail Injects 'Artificial Intelligence' Into Its CRM

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A machine learning feature will be coming to Redtail Technology's popular CRM program sometime this winter, according to company's CEO Brian McLaughlin. He made the announcement at Riskalyze's Fearless Investing Summit in San Antonio, Texas on Thursday. Billed as artificial intelligence, the new feature will provide advisors with three specific, actionable feedback buckets: sentiment, keyphrases and entities, or tags, such as specific types of investment accounts. The project, developed primarily from open source technology, has been in the works for nearly 18 months. When work began, the company looked to Amazon and Google's natural language processing libraries, but found they were too general and not specific enough to the financial services industry.


The Evolution of Self-Driving IT Ops

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Self-driving cars are making headlines every day; the future being envisioned as a car that runs itself, maintains itself, sends alerts when help is needed, and prevents accidents. While opinions of a self-driving car vary from excitement about simplifying the daily commute to "no way would I ever put total control in the hands of a machine," the concept gives rise to thoughts about self-driving data centers. What would they look like and how would they change IT as we know it? Reports indicate that enterprises are losing $21.8 million per year on average in downtime and 87 percent expect this to increase1. For organizations that are trying to manage and optimize increasingly complex hybrid IT environments that span mainframe and multi-cloud infrastructures, could evolving to a self-driven data center provide the keys to driving smarter, faster IT operations and preventing downtime?


Asset Management Social Market Analytics, Inc.

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Social Market Analytics, Inc. (SMA) aggregates the intentions of professional investors as expressed on Twitter & StockTwits and publishes a series of metrics that describes the current conversation relative to historical benchmarks. Our data is a leading indicator of price movement both positive and negative. There is unique predictive information in unstructured content. Social Market Analytics use AI and Machine Learning techniques developed over the last eight years to convert this unstructured content into data suitable for quantitative analysis. This opens a whole new area of big data analysis.


What we learn from AI's biases

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In "How to Make a Racist AI Without Really Trying," Robyn Speer shows how to build a simple sentiment analysis system, using standard, well-known sources for word embeddings (GloVe and word2vec), and a widely used sentiment lexicon. Her program assigns "negative" sentiment to names and phrases associated with minorities, and "positive" sentiment to names and phrases associated with Europeans. Even a sentence like "Let's go get Mexican food" gets a much lower sentiment score than "Let's go get Italian food." That result isn't surprising, nor are Speer's conclusions: if you take a simplistic approach to sentiment analysis, you shouldn't be surprised when you get a program that embodies racist, discriminatory values. It's possible to minimize algorithmic racism (though possibly not eliminate it entirely), and Speer discusses several strategies for doing so.


How artificial intelligence in customer service improves CX

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Consumers have more ways than ever to communicate with the brands they buy -- be it through private chat or in public on social media sites such as Twitter. If a conversation conveys a negative sentiment, it can be detrimental if it's not addressed quickly. Many companies are leaning on early stage AI tools for help. Companies can use artificial intelligence in customer service to build a brand that's associated with excellent customer experience (CX). This is critically important in an era in which consumers can easily compare product prices on the web, said Gene Alvarez, a Gartner managing VP, during a September 2018 webinar in which analysts discussed ways artificial intelligence in customer service can drive business growth.


Curious Case of Actuarial Science, Geocoding, and Machine Learning - DZone AI

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This article illustrates how Geocoding uncovers the untapped value within generally overlooked insurance categories, such as Life and Annuity, and how it can help address modern-day business challenges remarked by Orszag. While Geocoding in Big Data is gaining prominence within Property and Casualty (P&C), we believe the real opportunity lies in the actuarial adoption of AI framework capable of processing consumable inputs that weren't visible in the erstwhile "Ease of Geocoding" era. Establishing this premise for Life and Annuity, we then pivot towards crafting a general purpose Geo-inclusive architecture that can help actuaries of all disciplines apply Machine Learning to solve new generation of business problems, such as, dwindling subscribers or risk-attributed challenges, such as, Adverse Selection. Nearly all of the data in the insurance business has a location attribute, e.g. However, many insurance companies have not fully utilized this component besides billing and mailing purposes.


The 50 Best Public Datasets for Machine Learning – Stacy Stanford – Medium

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First, a couple of pointers to keep in mind when searching for datasets. Kaggle: A data science site that contains a variety of externally contributed interesting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even seattle pet licenses. Although the data sets are user-contributed, and thus have varying levels of cleanliness, the vast majority are clean. This site makes it possible to download data from multiple US government agencies.


Comparison of the Most Useful Text Processing APIs – ActiveWizards: machine learning company – Medium

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Nowadays, text processing is developing rapidly, and several big companies provide their products which help to deal successfully with diverse text processing tasks. In case you need to do some text processing there are 2 options available. The first one is to develop the entire system on your own from scratch. This way proves to be very time and resource consuming. On the other hand, you can use the already accessible solutions developed by well-known companies. This option is usually faster and simpler.


Analyzing verbatim comments in spreadsheets using machine learning

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Machine learning has made it more accessible to create meaningful insights in a data-rich world. This includes data from customer surveys, qualitative primary research, and online verbatim comments. There is a wide range of input that arises in the lifetime of a business. This data needs to be mined for actionable insights, that can significantly impact the brand value of a business. You could have launched a new marketing campaign and want to review customer sentiment.


ML.NET Sentiment Analysis with MongoDB – Hacker Noon

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Earlier this year (May 2018) Microsoft announced ML.NET, an open source and cross-platform machine learning framework built for .NET developers. It is exciting news to be able to integrate custom machine learning with .NET/C# applications. Although ML.NET is still in preview release version 0.5.0 at the time of writing, you can test drive it to explore the potential power of the framework. There are already a number of tutorials for ML.NET available from Microsoft and third parties. However, the example data sources are mostly flat files in the format of TSV (Tab Separated Values).