EmoLabel: Semi-Automatic Methodology for Emotion Annotation of Social Media Text

Canales, Lea; Daelemans, Walter; Boldrini, Ester; Martínez-Barco, Patricio

doi:10.1109/taffc.2019.2927564

Cited by 13 publications

(8 citation statements)

References 49 publications

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“…Users have experienced text communication in Web 1.0, video interactions in Web 2.0, and now are moving toward avatar activities in Web 3.0 [52], [53], [54], [55]. It is how users interact on the Internet and the deep integration of physical and social spaces within cyberspace that have been changing.…”

Section: A Parallel Intelligencementioning

confidence: 99%

ParaDefender: A Scenario-Driven Parallel System for Defending Metaverses

Han

Chen

Liu

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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The metaverse, as an instance of cyber-physicalsocial systems (CPSS) that originates in cyber-physical systems (CPS), features growing complexity, and diversity in terms of functionalities, as well as the exponentially increasing demand in network bandwidth and computational resources, thereby leading to exaggerated security threats. However, compared with the extensive attention received by the metaverse, solutions defending against the threats have not kept pace. A major obstacle to such solutions is virtuality-reality-synthesized threats. Therefore, it is imperative to design new paradigms to defend the metaverse effectively. In this article, we advance a parallel system, dubbed ParaDefender, to defend the metaverse against emerging new threats effectively. Inspired by parallel intelligence, ParaDefender comprises artificial cyberspace, computational experiments, and parallel execution. The basic idea is to make artificial and real cyberspaces executed in parallel to mutually guide each other for enhanced security, wherein the parallel execution is scenario driven in the sense that the scenarios originate from all possible spatial-temporal combinations of security threats in the metaverse. We also demonstrate how to land ParaDefender onto real-world applications, including the Industrial Internet of Things (IIoT) security operation application in the industrial metaverse, and the social governance application.

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Section: A Parallel Intelligencementioning

confidence: 99%

ParaDefender: A Scenario-Driven Parallel System for Defending Metaverses

Han

Chen

Liu

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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“…To reduce the feature dependency multiple kernel mechanism is selected. This mechanism faces an issue with labeling, thus [19] introduces Ecolabels which is a semiautomatic mechanism for the textual ER to provide the large-scale annotation considering English emotion corporate in different generations aiming robust reliability. It comprises two steps, first step includes the automatic process of pre-annotation of the unlabeled sentence along with optimized emotional categories.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

Al-Absi¹,

Kang²,

Al-Absi³

2023

Computers, Materials &Amp; Continua

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Sentiment analysis (AS) is one of the basic research directions in natural language processing (NLP), it is widely adopted for news, product review, and politics. Aspect-based sentiment analysis (ABSA) aims at identifying the sentiment polarity of a given target context, previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy. Hence this research work develops a deep-semantic and contextual knowledge networks (DSCNet). DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects. At first temporal relationships are established then deep semantic knowledge and contextual knowledge are introduced. Further, a deep integration layer is introduced to measure the importance of features for efficient extraction of different dimensions. Novelty of DSCNet model lies in introducing the deep contextual. DSCNet is evaluated on three datasets i.e., Restaurant, Laptop, and Twitter dataset considering different deep learning (DL) metrics like precision, recall, accuracy, and Macro-F1 score. Also, comparative analysis is carried out with different baseline methods in terms of accuracy and Macro-F1 score. DSCNet achieves 92.59% of accuracy on restaurant dataset, 86.99% of accuracy on laptop dataset and 78.76% of accuracy on Twitter dataset.

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“…Emotion classification is the automated process of identifying sentences in context and labeling them as happy, bored, neutral etc., based on the expression with words related to the specific emotions. Emolable is a Semi-Automatic Methodology for Emotion Annotation of Social Media Text, which aims to extract emotion from the information resources [7].…”

Section: Research For Emotion Classification Via Textmentioning

confidence: 99%

Weibo Depression Posts Detection by Natural Language Processing

Li¹,

Liu²,

Yin³

et al. 2022

HSET

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The goal of this paper is to detect depression based on the posts on social media. The dataset combined both tweet dataset and Weibo dataset scraped from the social media. To classify emotions, researchers have been using traditional models such as Bagging, Support Vector Machines, Decision tree, Multinomial Naïve Bayesian and K-nearest neighbor. In this paper, K- nearest neighbor is chosen based on the better precision in result. The main challenge is Chinese context translation and complexity of the context of the post. Finally, an UI page is designed to complete the mission to input a Weibo ID and output of the depression classification. Our approach achieves relatively higher quality results compared to the previous models in literature, while combining depression detection with the real-time social media posts. With this system, we demonstrate the practicability of our project by predicting the depression situation of Internet users through the model, to bring help to the depressed people or people with potential depression tendencies in society.

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EmoLabel: Semi-Automatic Methodology for Emotion Annotation of Social Media Text

Cited by 13 publications

References 49 publications

ParaDefender: A Scenario-Driven Parallel System for Defending Metaverses

ParaDefender: A Scenario-Driven Parallel System for Defending Metaverses

Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

Weibo Depression Posts Detection by Natural Language Processing

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