Discourse & Dialogue


Social Media Sentiment Analysis, and Soccer Meltwater

#artificialintelligence

Before delving into the nitty gritty of exactly how sentiment analysis works, let's break the concept down into something a little more tangible, shall we. Have you ever wondered what the South African public thought about, let's say, Iceland's football team defeating England in the Euro 2016? Well, that right there my friends, is why sentiment analysis software exists – to make vast quantities of data easily understandable at a glance. Think of it like a snapshot of the emotional response to a given topic. You might be asking yourself, but what about online surveys and polls, isn't that their purpose?


r/MachineLearning - [D] Email data set for sentiment analysis demos

#artificialintelligence

Does anyone know of any large email data sets (that are not Enron) hopefully something over the last few years or so. We are trying to work on different platforms to test their sentiment analysis.


r/textdatamining - LDA in Python – How to grid search best topic models? (A Comprehensive LDA Tutorial)

#artificialintelligence

Yes, but it also groups different words that have the same base form. So biographies and texts about animals might be wrongly grouped together, introducing noise in the corpus. I suspect, depending on the language, that this can happen a lot (or not) and greatly influence the process. I know it helps in Finnish and French and doesn't help in Swedish (with the texts I've used; I have compared LDA output on lemmatised and non-lemmatised versions of the same corpus), I was wondering if you had experience with other languages?



Does Google Duplex Scare You? – Behavioral Signals - Conversational AI – Medium

#artificialintelligence

Just in case you have not listened to the telephone calls placed by Google's latest conversational application code-named "Duplex" you should. Google Duplex can place calls and make restaurant reservations or book appointments to hair salons. Speech recognition and spoken dialogue technology has had the capability of successfully completing such tasks for over a decade now. What is exciting and frankly also a bit scary is that for the few (probably cherry-picked) recordings made available at the Google AI blog, the robotic caller is practically indistinguishable from a human. Google Duplex is not the first conversational system that passes the Turing test with flying colors for a few minutes: chatbots have been competing in this arena for years now.


Python Algo Trading: Market Neutral Hedge Fund Strategy

@machinelearnbot

Update 23 Aug 2017: Do note that Quantopian platform will no longer support third party broker integration. Please see their website under forum. The title of the post is "Phasing Out Brokerage Integrations". This course provides you with the tools that top hedge funds used. These institutional tools include but are not limited to market data, fundamental data, sentiment analysis data, and more.


Ranking the World's Top CEOs Using Social Media Sentiment Data - Dataconomy

#artificialintelligence

CEOs of the world's leading companies have a global influence that stretches beyond their own business and commercial interests. The general public is increasingly looking to the people steering some of the largest companies in the world for their views on political and social issues. In a Financial Times article published last August, Rana Foroohar describes today's chief executives as "transnational leaders" who face a growing expectation, from both investors and the public, to speak out on issues. Given this growing public expectation, our team at BrandsEye decided to apply our combination of machine learning algorithms and human intelligence to assess public sentiment towards executives on Twitter. Rather than using the typical financial indicators that so often inform these indices, our analysis was based on the unsolicited views of Twitter users.


MAQ Software Data Management, Power BI, Artificial Intelligence

#artificialintelligence

Our client hosts a large annual conference of 20,000 technical decision makers, IT professionals, and software developers from around the world. The conference includes over 700 sessions across multiple days that range from product demos to insights from industry leaders. Selected sessions from the annual event are repeated in smaller events in cities around the world. Each conference event generates a lot of feedback from attendees. The conference organizers analyze the feedback to determine whether each day was a success.


NASCIO Midyear 2018: Utah Finds Value in Data Analysis Through Machine Learning

#artificialintelligence

For several years, the state of Utah was collecting statistics and feedback on public opinion, but the state didn't really have a plan for what to do with the data. Recently, it decided to use machine learning tools to analyze health, transportation, air quality and geo-based Twitter information to perform sentiment analysis before, during and after Utah's winter inversions and air quality spikes. Utah CIO Michael Hussey explained how the state went about it at the 2018 National Association of State Chief Information Officers (NASCIO) Midyear Conference in Baltimore on Tuesday. Winter inversions in Utah occur when the usual atmospheric conditions become inverted. A dense layer of cold air becomes trapped under a layer of warm air, essentially sealing pollutants closer to the ground.


Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation

arXiv.org Artificial Intelligence

The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoder-decoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable generation.