What initially began as a Facebook event has morphed into a cultural moment, a juxtaposition of the previous day's inauguration of America's 45th president, Donald Trump. Heather Whaling is CEO of Geben Communication, a PR and social media agency with offices in Columbus, Ohio, and Chicago. She serves on the board of The Women's Fund of Central Ohio, mentors women entrepreneurs, and is a vocal advocate for paid parental leave. On the issues, it's increasingly difficult to find commonalities between Trump supporters and the marchers who will flock to DC and other cities around the country. Yet both groups share at least one tool in their toolbox: A mastery of social media as the go-to channel to amplify viewpoints and shape perceptions.

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network.

The Consortium for Lexical Research was operated by the Computing Research Laboratory at New Mexico State University, until December 1, 1995, when it ceased operation due to lack of funding support. The Consortium maintained a collection of lexical resources that were available to members. Now that the center no longer exists, CRL has made the files of CLR available free of charge to all interested parties. Although the resources are no longer maintained and updated, and hence can become out of date, they are still a very valuable source of information for information extraction system building.

I've made an interesting observation recently while talking to people about Thinkudo Enlighten. It regards the misunderstanding between Text Analytics and Machine (automated) Translation. More than once people've asked "How did you do the Chinese translation?" So in this post, I'd like clarify the difference between them. Whether or not Machine Translation should be a substudy of Text Analytics, I will leave it to the readers within academia to discuss.

Find out how AI, Machine Learning and Sentiment Analysis are being applied to Finance in a new conference organized by UNICOM Seminars Ltd in Hong Kong on 14-15 March 2017. Technology innovations meet greatest success in business when these are entirely'client focussed'. Developments in the retail sector, which is consumer-led, are addressing client demand for more personalised, faster and competitive services. Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which these services are offered. In particular, Financial Organisations are creating and leveraging such innovation in the domain of wealth management.

We live in an age where there is a vast, over-abundance of data available on the web. The problem is that sifting through all of it to find and make sense of whatever is deemed relevant is an incredibly time-consuming task. But it may soon become easier, as Massachusetts Institute of Technology researchers recently revealed in a paper that introduces a new artificial intelligence system that would be capable of learning, on its own, in extracting useful information from online sources. Recently presented at the conference of the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing in Austin, the researchers' paper describes a new information extraction system that's able to automatically extract structured information from unstructured machine-readable documents. Put simply, the program can do what humans are good at: When faced with a gap in information or something we don't understand, we go and search for another document to digest that will add to our understanding or further our knowledge.

Scaling a model in response to user demand is crucial for bringing a machine learning model into production. In this blog post, we follow up on our previous post by showing how to scale this model in production using Pivotal Cloud Foundry (PCF). Pivotal Cloud Foundry makes it easy to scale an application using the command line interface (CLI) or the Apps Manager with no downtime. We utilize Apps Manager to horizontally scale out (spinning up new instances of our model) our application automatically utilizing PCF's load balancer, which reroutes new requests to appropriate instances of our model. Using the sentiment analysis analysis model we've built with Pivotal Greenplum and Python, we built a dashboard for analyzing live Tweets from the Twitter firehose.

This is the continuation of my mini-series on sentiment analysis of movie reviews, which originally appeared on recurrentnull.wordpress.com. Last time, we had a look at how well classical bag-of-words models worked for classification of the Stanford collection of IMDB reviews. As it turned out, the "winner" was Logistic Regression, using both unigrams and bigrams for classification. The best classification accuracy obtained was .89 So, bag-of-words models may be surprisingly successful, but they are limited in what they can do.

Looking at this text, we already see complexity emerging. As a human reader, I'm sure you'll say this is a negative review, and undoubtedly there are some clearly negative words ("dreadful", "confusing", "terrible"). But to a high degree, negativity comes from negated positive words: "lacking achievement", "wasn't very funny", "not as good as she could have given". So clearly we cannot just look at single words in isolation, but at sequences of words – n-grams (bigrams, trigrams, …) as they say in natural language processing. The question is though, at how many consecutive words should we look?

Text mining predictive methods help organizations enhance the value of unstructured information by deploying insight from text analysis in software applications and business processes. Once textual information is transformed into a set of structured data using text mining (or text analytics) it can be combined with traditional data mining algorithms to generate new insight for sentiment analysis and predictive analytics. Whether it is marketing and competitive intelligence, customer relationship management, social media monitoring, operational risk mitigation or threat discovery, big data is a key element for understanding where you are and where you're going. Text mining predictive methods support organizations in staying competitive. It helps them improve the ability to quickly react to customer feedback, market changes, competitive landscape evolutions, etc. This is precisely why enterprises should embed text analytics and predictive analytics into their business processes.