Can we use machine learning algorithms to better understand consumers? How they truly feel, simply by looking at their face or reading their Tweets?

Abstract: With the rapid growth in the number of scientific publications, year after year, it is becoming increasingly difficult to identify quality authoritative work on a single topic. Though there is an availability of scientometric measures which promise to offer a solution to this problem, these measures are mostly quantitative and rely, for instance, only on the number of times an article is cited. With this approach, it becomes irrelevant if an article is cited 10 times in a positive, negative or neutral way. In this context, it is quite important to study the qualitative aspect of a citation to understand its significance. This paper presents a novel system for sentiment analysis of citations in scientific documents (SentiCite) and is also capable of detecting nature of citations by targeting the motivation behind a citation, e.g., reference to a dataset, reading reference. Furthermore, the paper also presents two datasets (SentiCiteDB and IntentCiteDB) containing about 2,600 citations with their ground truth for sentiment and nature of citation. SentiCite along with other state-of-the-art methods for sentiment analysis are evaluated on the presented datasets. Evaluation results reveal that SentiCite outperforms state-of-the-art methods for sentiment analysis in scientific publications by achieving a F1-measure of 0.71. 1 INTRODUCTION Sentiment analysis is the process of computationally categorizing and identifying opinions present in a textual document or images. As a field, sentiment analysis has been gaining a lot of interest from the scientific community in recent years. The main motivation for this work comes from the author's observation that there is an unavailability of a system capable of automatically analyzing the sentiment present in citations of scientific publications.

Gunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazon-selected expert conversationalists. We focus on understanding complex sentences and having in-depth conversations in open domains. In this paper, we introduce some innovative system designs and related validation analysis. Overall, we found that users produce longer sentences to Gunrock, which are directly related to users' engagement (e.g., ratings, number of turns). Additionally, users' backstory queries about Gunrock are positively correlated to user satisfaction. Finally, we found dialog flows that interleave facts and personal opinions and stories lead to better user satisfaction.

AI has been a popular topic for movies and fascinated audiences for over 60 years. Hollywood hasn't always gotten it right -- in fact, most of the time, they've been totally off. What is it about artificial intelligence that entices directors? What are they missing when they create movies about AI? Let's take a look at a few popular movies about AI, why the topic is popular, why some got it wrong, and a few movies that actually got it right. To most people, artificial intelligence creates a question of what is and what isn't human.

CHROs and employees who participate in upskilling programs are learning more about how AI and machine learning can help them in their jobs.

Machine learning is helping marketers to go beyond existing marketing strategies and make precise decisions based on data. The collection, processing, and use of data have become strategically imperative that drives better businesses. This data has allowed machine learning to minimise the gap between strategic models and the actual outcomes of marketing campaigns. According to Forrester's Global State of AI Online survey, the most prominent strategic growth was observed in customer experience and support (57%). This benefit was followed by the company's ability to deliver better products and services.

I have a dataset of 8 million unique members, approximately 800 million records. Of those 8 million members I have a positive sample of about 25000. I would like to not simply downsample although the downsampled RF performs pretty well. The data is on a Hadoop cluster. I only have access to it via a Zeppelin notebook with PySpark.

A picture of British mathematician Alan Turing hangs behind one of his notebooks during an auction preview in 2015. Turing argued that the ultimate test of a computer's intelligence was whether it could communicate with a human in a way indistinguishable from another human mind. Increasingly, AI-generated writing is making researchers think again about what the test really means. Jacob Berkowitz is a writer in Almonte, Ont., the founder of Quantum Writing and a writer-in-virtual-residence at University of Ottawa's Institute for Science, Society and Policy. I remember, clearly, my son's first word.