MarketResearchNest.com adds Global Artificial Intelligence in Medical Imaging Market 2019 by Company, Regions, Type and Application, Forecast to …

New technologies, such as machine learning and artificial intelligence (AI), can transform the system.

Initially conceived as a technology that could mimic human intelligence, AI hasn't quite achieved this feat but significant progress has been made.

The future of CX will likely involve machines reading people's faces, … reactions via deep learning techniques such as AI, machine learning, visual …

When I do a proposition bet, I tie the wager to my fantasy gradings produced by my machine learning tools.

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.