Text analytics can be a bit overwhelming and frustrating at times with the unstructured and noisy nature of textual data and the vast amount of information available. "Text Analytics with Python" published by Apress\Springer, is a book packed with 385 pages of useful information based on techniques, algorithms, experiences and various lessons learnt over time in analyzing text data. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization.

Sentiment analysis is one of the most prevalent uses of text analytics, but the technology has many other valuable uses. Text analytics finds a range of applications in scientific, medical and technology development. It can detect root causes of events and augment the knowledge of what happened with an understanding of why it happened. When used predictively, it can help anticipate future outcomes and prevent adverse events. Text analytics can also enable process automation and case management.

For our case we only consider Text feature of the Tweet as we are interested on the review of the movie. We can also use the other features such as Latitude/Longitude, replied to, etc. do other analysis on the tweeted data.

If you have a 4G plan with Virgin Mobile, you can now access Twitter without diving in to your monthly data allowance. That means you can scroll through your feed, check your mentions and respond to pressing Direct Messages without fear of incurring any charges. The "data-free" access joins Facebook Messenger and WhatsApp, which the company first offered to subscribers last November. The only catch is that you can't stream live video through the app -- so if you want to watch the news or catch up with the day's Wimbledon action, you'll need to look elsewhere. Virgin Media says the expansion is part of a larger "plan" to offer data-free social messaging.

As AWS continues to support the Artificial Intelligence (AI) community with contributions to Apache MXNet and the release of Amazon Lex, Amazon Polly, and Amazon Rekognition managed services, we are also expanding our team of AI experts, who have one primary mission: To lower the barrier to AI for all AWS developers, making AI more accessible and easy to use. At ISI, she spearheaded multimillion-dollar research grants funded by the Defense Advanced Research Projects Agency (DARPA) and Intelligence Advanced Research Projects Activity (IARPA). The research focused on topics such as machine reading, which aims at teaching machines to read and understand text just like humans do; information extraction from unstructured documents on the Web; metaphor interpretation; and sentiment analysis. Product Marketing Manager for the AWS AI portfolio of services which includes Amazon Lex, Amazon Polly, and Amazon Rekognition, as well the AWS marketing initiatives with Apache MXNet.

Extracting value from a universe of data, analysing sentiment around company names (equities) or about anything else (macro), is a complex journey and we are only about 5% down that road. The parameters are evolving by which an ever-expanding data set, including the likes of Twitter, pictures, text, video is processed; relying on experts versus the wisdom of the crowd; sentiment derived from a "bag of words", as opposed to structured linguistic analysis. Last week's Unicom conference, AI, Machine Learning and Sentiment Analysis Applied to Finance (July 14) brought together a group of experts in this area. Professor Gautum Mitra, OptiRisk Systems introduced Elijah DePalma and James Cantarella, Thomson Reuters; Pierce Crosby, StockTwits; Anders Bally, Sentifi; Peter Hafez, RavenPack; Stephen Morse, Twitter. DePalma differed somewhat from the others because the Thomson Reuters sentiment engine uses only accredited Reuters news data, rather than raw social media chatter.

Extracting value from a universe of data, analysing sentiment around company names (equities) or about anything else (macro), is a complex journey and we are only about 5% down that road. The parameters are evolving by which an ever-expanding data set, including the likes of Twitter, pictures, text, video is processed; relying on experts versus the wisdom of the crowd; sentiment derived from a "bag of words", as opposed to structured linguistic analysis. Last week's Unicom conference, AI, Machine Learning and Sentiment Analysis Applied to Finance (July 14) brought together a group of experts in this area. Professor Gautum Mitra, OptiRisk Systems introduced Elijah DePalma and James Cantarella, Thomson Reuters; Pierce Crosby, StockTwits; Anders Bally, Sentifi; Peter Hafez, RavenPack; Stephen Morse, Twitter. DePalma differed somewhat from the others because the Thomson Reuters sentiment engine uses only accredited Reuters news data, rather than raw social media chatter.

Germany may soon launch an investigation into Facebook over the social network's broad privacy policy that allows it to collect massive amounts of information from users. They, in part, blame the "fine print" of Facebook's terms of service. The Federal Cartel Office, Germany's national competition regulator, believes Facebook is "extorting" its users by making them agree to terms and conditions they may not fully understand in order to use the popular service. German regulators have also floated the possibility that anti-trust actions could use this angle in the courts. Read: Why Was Google Fined $2.7 Billion By The European Union?

Statistical tagging offers insights from various levels of granularity starting from basic text classification, sentiment analysis to deep information extraction and topic modeling/ automated summation. The HR familiarity with basic Boolean keyword searches to identify good resumes is a very good example of symbolic tagging. However, for the sake of familiarity let's take the example of resume scoring in Hiring on a large unstructured dataset Apart from resume/ application scoring, "Conditional rules models" can also help identify complex human language expressions. NLP vendors typically offer a combination of services mentioned above, including summation, topic modeling, and conditional rules models.

Conference Topics Topics at this conference include, but are not limited to: Business Analytics - Methods: Dimensionality Reduction, Feature Extraction, and Feature Selection Supervised, Semi-Supervised, and Unsupervised Methods Statistical Learning Theory Online Learning, Data Stream Mining, and Dynamic Data Mining Graph Mining and Semi-Structured Data patial and Temporal Data Mining Deep Learning and Neural Network Research Large Scale Data Mining Uncertainty Modeling in Data Mining Business Analytics - Applications: Credit Scoring and Financial Modeling Forecasting Fraud Detection Web Intelligence and Information Retrieval Marketing, Business Intelligence, and e-Commerce Decision Analysis and Decision Support Systems Social Network Analysis Privacy-preserving Data Mining and Privacy-related Issue Text Mining, Sentiment Analysis, and Opinion Mining Important Dates July 31, 2017: Deadline for submission of extended abstracts August 15, 2017: Accept/reject decision November 15, 2017: Deadline for early registration January 17-19, 2018: BAFI 2018 *Only one contributed abstract is accepted from the same presenting author. Submission Guidelines Authors are requested to submit a 600 word abstract in English using the platform available at the EasyChair system. Please do not attach any additional files at this time.