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Newton Howard
Researcher at University of Oxford
Publications - 120
Citations - 3536
Newton Howard is an academic researcher from University of Oxford. The author has contributed to research in topics: Sentiment analysis & Artificial neural network. The author has an hindex of 24, co-authored 118 publications receiving 2664 citations. Previous affiliations of Newton Howard include University of Toulouse & Massachusetts Institute of Technology.
Papers
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Journal ArticleDOI
Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining
Soujanya Poria,Alexander Gelbukh,Amir Hussain,Newton Howard,Dipankar Das,Sivaji Bandyopadhyay +5 more
TL;DR: The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label by way of concept-based opinion mining.
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Fusing audio, visual and textual clues for sentiment analysis from multimodal content
TL;DR: This paper proposes a novel methodology for multimodal sentiment analysis, which consists in harvesting sentiments from Web videos by demonstrating a model that uses audio, visual and textual modalities as sources of information.
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The use of photoplethysmography for assessing hypertension
Mohamed Elgendi,Richard Fletcher,Yongbo Liang,Newton Howard,Nigel H. Lovell,Derek Abbott,Kenneth Lim,Rabab K. Ward +7 more
TL;DR: Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.
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Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study
Adnan Amin,Sajid Anwar,Awais Adnan,Muhammad Nawaz,Newton Howard,Junaid Qadir,Ahmad Hawalah,Amir Hussain +7 more
TL;DR: The empirical results demonstrate that the overall predictive performance of MTDF and rules-generation based on genetic algorithms performed the best as compared with the rest of the evaluated oversampling methods and rule-generation algorithms.
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Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis
TL;DR: A multi-modal affective data analysis framework is proposed to extract user opinion and emotions from video content and outperforms the state-of-the-art model in multimodal sentiment analysis research with a margin of 10–13% and 3–5% accuracy on polarity detection and emotion recognition, respectively.