Author name cluster

Newton Howard

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4 papers

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IS Journal 2025 Journal Article

Explicable Artificial Intelligence for Affective Computing

Rui Mao
Erik Cambria
Yang Li
Newton Howard

Artificial intelligence (AI) is increasingly tasked with recognizing and responding to human emotions, making affective computing one of its most consequential frontiers. As AI spreads into finance, policymaking, and mental health, the opacity of deep learning models raises urgent challenges for trust, accountability, and ethics. This special issue addresses explicability not just as algorithmic transparency, but as a paradigm integrating cognitive science, the humanities, and ethical foresight with technical innovation. Guided by the “Seven Pillars for the Future of AI”— multidisciplinarity, task decomposition, parallel analogy, symbol grounding, similarity measure, intention awareness, and trustworthiness—it envisions affective AI as a partner in meaning-making rather than a mere inference engine. The six featured articles span topics from depression detection and sentiment analysis to hate speech moderation and interpretable driving behaviors, advancing affective AI that is accurate, interpretable, and aligned with human dignity.

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IS Journal 2014 Journal Article

Semantic Multidimensional Scaling for Open-Domain Sentiment Analysis

Erik Cambria
Yangqiu Song
Haixun Wang
Newton Howard

The ability to understand natural language text is far from being emulated in machines. One of the main hurdles to overcome is that computers lack both the common and common-sense knowledge that humans normally acquire during the formative years of their lives. To really understand natural language, a machine should be able to comprehend this type of knowledge, rather than merely relying on the valence of keywords and word co-occurrence frequencies. In this article, the largest existing taxonomy of common knowledge is blended with a natural-language-based semantic network of common-sense knowledge. Multidimensional scaling is applied on the resulting knowledge base for open-domain opinion mining and sentiment analysis.

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AAAI Conference 2013 Conference Paper

Automatic Identification of Conceptual Metaphors With Limited Knowledge

Lisa Gandy
Nadji Allan
Mark Atallah
Ophir Frieder
Newton Howard
Sergey Kanareykin
Moshe Koppel
Mark Last

Full natural language understanding requires identifying and analyzing the meanings of metaphors, which are ubiquitous in both text and speech. Over the last thirty years, linguistic metaphors have been shown to be based on more general conceptual metaphors, partial semantic mappings between disparate conceptual domains. Though some achievements have been made in identifying linguistic metaphors over the last decade or so, little work has been done to date on automatically identifying conceptual metaphors. This paper describes research on identifying conceptual metaphors based on corpus data. Our method uses as little background knowledge as possible, to ease transfer to new languages and to minimize any bias introduced by the knowledge base construction process. The method relies on general heuristics for identifying linguistic metaphors and statistical clustering (guided by Wordnet) to form conceptual metaphor candidates. Human experiments show the system effectively ﬁnds meaningful conceptual metaphors.

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IS Journal 2013 Journal Article

Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

Soujanya Poria
Alexander Gelbukh
Amir Hussain
Newton Howard
Dipankar Das
Sivaji Bandyopadhyay

SenticNet 1. 0 is one of the most widely used, publicly available resources for concept-based opinion mining. The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label.

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