Journal ArticleDOI
YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context
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TLDR
Experimental results indicate that training on written movie reviews is a promising alternative to exclusively using (spoken) in-domain data for building a system that analyzes spoken movie review videos, and that language-independent audio-visual analysis can compete with linguistic analysis.Abstract:
This work focuses on automatically analyzing a speaker's sentiment in online videos containing movie reviews. In addition to textual information, this approach considers adding audio features as typically used in speech-based emotion recognition as well as video features encoding valuable valence information conveyed by the speaker. Experimental results indicate that training on written movie reviews is a promising alternative to exclusively using (spoken) in-domain data for building a system that analyzes spoken movie review videos, and that language-independent audio-visual analysis can compete with linguistic analysis.read more
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An Atlas of Cultural Commonsense for Machine Reasoning.
TL;DR: This work introduces an approach that extends prior work on crowdsourcing commonsense knowledge by incorporating differences in knowledge that are attributable to cultural or national groups, and moves a step closer towards building a machine that doesn't assume a rigid framework of universal Commonsense knowledge, but rather has the ability to reason in a contextually and culturally sensitive way.
Journal ArticleDOI
Inferring Sentiments from Supervised Classification of Text and Speech cues using Fuzzy Rules
Srishti Vashishtha,Seba Susan +1 more
TL;DR: A supervised fuzzy rule-based system for multimodal sentiment classification, that can identify the sentiment expressed in video reviews on social media platform, that is compared with eight state-of-the-art techniques for supervised machine learning.
Journal ArticleDOI
A Survey of Computational Approaches and Challenges in Multimodal Sentiment Analysis
Journal ArticleDOI
Machine Learning in Detecting COVID-19 Misinformation on Twitter
TL;DR: In this paper, the authors proposed three effective misinformation detection models: Long Short Term Memory (LSTM) networks, multichannel convolutional neural network (MC-CNN), and k-nearest neighbors (KNN).
Book
Multimodal Sentiment Analysis
TL;DR: This chapter discusses the major research challenges in this topic followed by the overview of the proposed multimodal sentiment analysis framework.
References
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Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TL;DR: A simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (Thumbs down) if the average semantic orientation of its phrases is positive.
Proceedings Article
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TL;DR: This article proposed an unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended(thumbs down) based on the average semantic orientation of phrases in the review that contain adjectives or adverbs.
Correlation-based Feature Selection for Machine Learning
TL;DR: This thesis addresses the problem of feature selection for machine learning through a correlation based approach with CFS (Correlation based Feature Selection), an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy.
Proceedings ArticleDOI
A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
Bo Pang,Lillian Lee +1 more
TL;DR: This paper proposed a machine learning method that applies text-categorization techniques to just the subjective portions of the document, extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.
Journal ArticleDOI
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
TL;DR: In this paper, the authors discuss human emotion perception from a psychological perspective, examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data.