Text Analytics

Text Analytics

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Textual & Sentiment Analysis of Movie Reviews

Yousef Fadila

The Rock Breaker

How Social Media is Transforming CRM - Infographics

Last-mile technology is any telecommunications technology that carries signals from the broad telecommunication backbone along the relatively short distance (hence, the "last mile") to and from the home or business. Or to put it another way: the infrastructure at the neighborhood level. In many communities, last-mile technology represents a major remaining challenge because the cost of providing high-speed, high-bandwidth services to individual subscribers in remote areas can be higher than the service provider would like. Laying wire and fiber optic cables is an expensive undertaking that can be environmentally demanding and require high maintenance. Experts hope that broadband wireless networks will eventually provide the solution and meet everyone's needs.(techtarget.com)

Last Mile Access Technologies

R is a free, open-source environment for statistical analysis and graphing. In its almost 20 years of existence, R has remained popular in both academic and business environments. The newer Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. This session outlines functional and performance differences between these two software packages. You’ll see demonstrations of best tips for integrating this software with Windows and walk away with guidelines for working with commercial software. A version of this presentation had 100 attendees at the PASS Business Analytics Conference in Chicago (April 2013), and 40 attendees for the PASS Virtual Business Analytics meeting (May 2013).

Data analysis with R and Julia

Mark Tabladillo

Voices of Business: Our Journey and Lessons Learned

Delvinia

Applying large scale text analytics with graph databases

Marissa Kobylenski

IP Multicasting

Aspect Based Sentiment Analysis (ABSA) systems receive as input a set of texts (e.g., product reviews) discussing a particular entity (e.g., a new model of a laptop). The systems attempt to identify the main (e.g., the most frequently discussed) aspects (features) of the entity (e.g., battery, screen) and to estimate the average sentiment of the texts per aspect (e.g., how positive or negative the opinions are on average for each aspect).

Supervised Learning Based Approach to Aspect Based Sentiment Analysis

Structured Cabling Technologies for Networking

This R Programming Tutorial will unravel the complete Introduction to R, Benefits of R for Business, What is Sentiment Analysis?, Advantages & Applications of Sentiment Analysis. In addition, we will also extensively cover Data Collection & Results using Sentiment Analysis. At the end, you'll have strong knowledge regarding Sentiment Analytics via R Programming. PPT Agenda ✓ Introduction to R Programming ✓ R for Data Analysis ✓ What is Sentiment Analysis all about? ✓ How Sentiment Analysis works ✓ Real World Applications of R Sentiment Analysis ✓ Job Trends for R ---------- What is R Programming? R is a programming language and software environment for statistical computing and graphics. It is widely used among statisticians and data miners for data analysis and visualization. What is Sentiment Analysis? Sentiment analysis is the process of computing, identifying and categorizing opinions expressed in a blurb of text in order to determine whether a user's attitude towards a particular topic or product is positive, negative, or neutral. It uses natural language processing, text analysis and computational linguistics to identify and extract subjective information from text. ---------- Sentiment Analysis has the following components: 1. Collect Data from Desired Sources 2. Remove Sentiment Neutral Words 3. Two Way Categorization 4. Results are Positive on Negative 5. Act on the Model! ---------- Applications of Predictive Analysis 1. Analytical Customer Relationship Management (CRM) 2. Clinical decision support systems 3. Customer satisfaction & retention 4. Direct marketing 5. Fraud detection ---------- Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance. Email: sales@skillspeed.com Website: https://www.skillspeed.com

Sentiment Analysis via R Programming

Skillspeed

OpenText Content Analytics - Quick Introduction

Charles-Olivier Simard

Introduction to Text Mining

Minha Hwang

Industrial Disputes: Dispute Settlement Methods and Machinery

The last ten to fifteen years have seen a pervasive growth of the Internet both in terms of its depth of penetration into user population as well the breadth of areas into which Internet is now present. As Internet access becomes faster and applications move to the cloud the profile of Internet traffic continues to change. Peer to Peer traffic, video sharing and OTT (over the top) services coupled with almost ubiquitous access to high speed internet poses new challenges to service providers (how to better utilize bandwidth) as well OEMs (how to increase bits per second and packets per second through the equipment). A key to understanding and solving these challenges is to understand what constitutes Internet traffic and how the internet traffic will look like in the coming years and then based on that optimize networks and infrastructure to better utilize available resources. This is what this project aims to address i.e. understanding internet traffic from various perspectives (application, protocol, packet size and others) such that this understanding can then feed into network and infrastructure design. A data product named SITAPT (Study of Internet Traffic to Analyze and Predict Traffic) is built which addresses the aims of this project.

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Text Analytics

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IP Multicasting

Supervised Learning Based Approach to Aspect Based Sentiment Analysis

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Sentiment Analysis via R Programming

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Sentiment analysis

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Auto Trader is the UK’s leading digital automotive marketplace. We receive 60 million cross-platform visits each month, while our ML-powered car valuations provide 5.5 million valuations a month to both consumers and dealers. At Auto Trader, our development culture aims to combine a data-driven approach with continuous delivery. As such we are keen to ensure that any new machine learning models we develop fit this way of working; our Data Scientists should be able to directly build, train, re-train and deploy their models with minimal outside input. Reducing the time to live allows for more experimentation and reduces the cost of getting machine learning models into production. This talk will describe how we launched our new “Days to Sell” ML-powered metric, using Databricks notebooks to experiment with and tune our model, MLeap to serve out predictions in real time, and Kubernetes to automate the deployment of new models. We will show how we went from model experimentation and design, right through to deploying our model to a production environment. We will also share our thoughts on how we expect this approach to evolve in the future, including how we expect to integrate with MLflow.

How We Used Databricks, MLeap, and Kubernetes to Productionize Spark ML Faste...

Databricks

Are you familiar with the following recipe? - First define an API and its documentation. - Then take the API and create its server implementation. - Then create a client for the same API, but in JavaScript land. - Then imperatively modify the browser’s DOM in order to allow user to do and see stuff. Sounds about right? During this talk you will forget about all these things and become a functional web developer. I will show you how to write type-level web APIs, purely functional server and client implementations and how to render things without touching browser’s DOM. The talk is divided into two parts: backend & frontend. The first part of the talk will introduce the concepts behind haskell-servant and we will implement a simple web app backend. Then we will code the app itself using Elm and reactive approach. NOTE: I will also try to do a short intro to Haskell and Elm during the presentation and use as fewest constructs as possible.

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ReportsnReports

Niche bloggers up to multinational corporations, they are all interested in monitoring their web traffic and its patterns across time. Google Analytics is the most widely used solution to keep track of this type of data. It provides a UI for a wide range of reports and possibilities for various types of visualizations. Moreover, the availability of the Analytics API coupled with the corresponding R packages can now give more options for custom web analyses. The plan for this talk is to cover the following : • What is web analytics ? How it works ? • Interfacing with the Analytics Reporting API via an R package (RGA) • Practical analytics applications with R • Discussion

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