Affective Computing and Sentiment Analysis

@article{Cambria2016AffectiveCA,
  title={Affective Computing and Sentiment Analysis},
  author={E. Cambria},
  journal={IEEE Intelligent Systems},
  year={2016},
  volume={31},
  pages={102-107}
}
  • E. Cambria
  • Published 1 March 2016
  • Computer Science
  • IEEE Intelligent Systems
Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific… 
Sentic Computing for Social Network Analysis
Abstract Sentiment analysis and opinion mining have been acquiring a crucial role in both commercial and research applications because of their possible applicability to several different fields.
Emotion recognition and brain mapping for sentiment analysis: A review
The rapid growth of the Internet has caused the increase in the amount of textual information available, such as in blogs, discussion forums and review sites on the web, where the texts surely have
Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework
TLDR
This chapter aims to relate the sentiment-based characterization inferred from books with the distribution of emotions within the same texts, and consists in a method to compare and classify texts based on the feelings expressed within the narrative trend.
Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
TLDR
This thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA, and offers a review of ontology-based and lexicon-based SA along with machine learning models used to analyze the sentiment of the given context.
SENTIC COMPUTING FOR SOCIAL NETWORK ANALYSIS
Sentiment analysis or opinion mining can be defined as a particular application of data mining, which aims to aggregate and extract emotions and feelings from different types of documents [1]. The
Using Facebook Reactions to Recognize Emotion in Political Domain
Opinion Mining and Emotion Mining are part of the Sentiment Analysis area, but they have different objectives. Opinion Mining is concerned with the study of opinions expressed in texts and its basic
Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals
TLDR
The paper includes extensive reviews on different frameworks and categories for state-of-the-art techniques, critical analysis of their performances, and discussions of their applications, trends and future directions to serve as guidelines for readers towards this emerging research area.
A lightweight clustering–based approach to discover different emotional shades from social message streams
TLDR
The algorithm shows interesting results due to its intrinsic fuzzy nature that reflects the human feeling expressed in the text, often composed of a mix of blurred emotions, and at the same time, the benefits of the extended version yield better classification results.
How much do Twitter posts affect voters? Analysis of the multi-emotional charge with affective computing in political campaigns
TLDR
This research proposes an analysis of the emotional charge for the U.S. presidential elections in 2020, based on a hybrid approach that combines affective computing and classic statistical analysis, and analyzes the multi-emotional charge of candidates and voters.
Emotion Recognition from Text
TLDR
Various classifiers like Naive Bayes, Support vector machines, Neural networks, and their combinations were used to detect the human emotions and a recommendation approach is proposed based on the emotions from the text.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 62 REFERENCES
Meta-level sentiment models for big social data analysis
TLDR
A novel approach for sentiment classification based on meta-level features is proposed, which boosts existing sentiment classification of subjectivity and polarity detection on Twitter and offers a more global insight of the resource components for the complex task of classifying human emotion and opinion.
The Hourglass of Emotions
TLDR
A novel biologically-inspired and psychologically-motivated emotion categorisation model that represents affective states both through labels and through four independent but concomitant affective dimensions, which can potentially describe the full range of emotional experiences that are rooted in any of us.
Sentiment Analysis and Opinion Mining
  • Lei Zhang, B. Liu
  • Computer Science
    Encyclopedia of Machine Learning and Data Mining
  • 2017
TLDR
This book is a comprehensive introductory and survey text that covers all important topics and the latest developments in the field with over 400 references and is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular.
Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis
TLDR
A novel method to extract features from visual and textual modalities using deep convolutional neural networks and significantly outperform the state of the art of multimodal emotion recognition and sentiment analysis on different datasets is presented.
Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns
TLDR
An algorithm that assigns contextual polarity to concepts in text and flows this polarity through the dependency arcs in order to assign a final polarity label to each sentence is presented, which enables a more efficient transformation of unstructured social data into structured information, readily interpretable by machines.
Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis
TLDR
A new approach to phrase-level sentiment analysis is presented that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions, achieving results that are significantly better than baseline.
Towards multimodal sentiment analysis: harvesting opinions from the web
TLDR
This paper addresses the task of multimodal sentiment analysis, and conducts proof-of-concept experiments that demonstrate that a joint model that integrates visual, audio, and textual features can be effectively used to identify sentiment in Web videos.
Fusing audio, visual and textual clues for sentiment analysis from multimodal content
TLDR
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.
Aspect extraction for opinion mining with a deep convolutional neural network
TLDR
This paper used a 7-layer deep convolutional neural network to tag each word in opinionated sentences as either aspect or non-aspect word, and developed a set of linguistic patterns for the same purpose and combined them with the neural network.
Common Sense Knowledge Based Personality Recognition from Text
TLDR
It is observed that the use of common sense knowledge with affective and sentiment information enhances the accuracy of the existing frameworks which use only psycho-linguistic features and frequency based analysis at lexical level.
...
1
2
3
4
5
...