Affective Computing and Sentiment Analysis

By Erik Cambria. IEEE Intelligent Systems, 2016

1,417 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/d385f52c9ffbf4bb1d689406cebc5075f5ad4d6a

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Citations in the corpus

A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks (2016, background): “As the ﬁelds of affective computing and sentiment analysis have gained increasing popularity (Cambria, 2016), it is a major concern to detect sarcastic, ironic, and metaphoric expressions.”
Aspect extraction for opinion mining with a deep convolutional neural network (2016, background): “E There are a good number of companies, both large-and small-scale, that focus on the analysis of opinions and sentiments as part of their mission (Cambria, 2016).”
Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis (2016, background): “Sentiment extraction from text has made considerable progress in the past few years [1], [2].”
IoT-Enabled Social Relationships Meet Artificial Social Intelligence (2021, background): “[37], the field that encompasses the development and design of devices and systems that can capture, process and detect human effects, and Personality Computing [38], the study field that aim to integrate human personality traits in the computing systems, and many other social-related computing fiel”
Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling (2018, background): “In recent years, sentiment analysis has become increasingly popular for processing social media data on online communities, blogs, wikis, microblogging platforms, and other online collaborative media [2].”
Natural language based financial forecasting: a survey (2017, background): “Sentiment analysis [16] is a ‘suitcase research problem’ [19] that requires tackling many NLP sub-tasks, including aspect extraction [92], subjectivity detection [27], named entity recognition [73], and sarcasm detection [93], but also complementary tasks such as personality recognition [74], user p”
Physiological Signals-based Emotion Recognition via High-order Correlation Learning (2019, background): “When K grows in [1, 6], more complicated discriminative inter-sample correlation information is included by hypergraphs.”
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives (2016, background): “Sentiment analysis systems can be broadly categorized into knowledge-based or statistics-based systems (Cambria, 2016).”
Identifying the sentiment styles of YouTube’s vloggers (2018, background): “detecting political sentiments (Cambria, 2016).”
Machine Learning-Based Sentiment Analysis for Twitter Accounts (2018, background): “Similarly, another categorization has been presented [6] with the categories of statistical, knowledge-based and hybrid approaches.”
A Review of Sentiment Analysis Research in Chinese Language (2017, neutral): “Followed are two major methods used to perform sentiment classification for the monolingual approach; namely machine learning based and knowledge based [55], as shown in Fig.”
Radical-Based Hierarchical Embeddings for Chinese Sentiment Analysis at Sentence Level (2017, background): “However, none of the above embeddings have considered incorporating sentiment information and apply the radical embeddings to the task of sentiment classification (Cambria 2016).”
Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning (2020, background): “Developing AI systems that appear to be more humanlike in the responses they generate (Scheutz et al. 2007; Cambria 2016) can lead to the risk of associating a humanlike persona to AI systems, which in turn can lead to the disastrous consequence of a section of the society into (falsely) believing t”
Sentiment analysis using deep learning architectures: a review (2019, background): “Cambria (2016) presents a categorization for the tasks related to affective computing and sentiment analysis.”