Emotion Recognition for Vietnamese Social Media Text

Ho, Vong Anh; Nguyen, Duc Phuc; Nguyen, Danh Hoang; Pham, Linh Thi-Van; Nguyen, Duc-Vu; Nguyen, Kiet Van; Nguyen, Ngan Luu-Thuy

doi:10.1007/978-981-15-6168-9_27

Cited by 20 publications

(9 citation statements)

References 17 publications

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“…Another challenge is to update a model, especially in case of dynamic data updates [ 553 ]. The following datasets can help with the task of classifying different emotion types from multimodal sources such as physiological signals, audio content, or videos: BED [ 554 ], MuSe [ 555 ], MELD [ 544 , 556 ], UIT-VSMEC [ 411 ] HUMAINE [ 557 ], IEMOCAP [ 558 ], Belfast database [ 559 ], SEMAINE [ 560 ], DEAP [ 561 ], eNTERFACE [ 384 ], and DREAMER [ 562 ]. Github [ 563 ], for instance, provides a list of all public EEG-datasets such as High-Gamma Dataset (128-electrode dataset from 14 healthy subjects with about 1000 four-second trials of executed movements, 13 runs per subject), Motor Movement/Imagery Dataset (2 baseline tasks, 64 electrodes, 109 volunteers), and Left/Right Hand MI (52 subjects).…”

Section: Resultsmentioning

confidence: 99%

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Kaklauskas

Abraham

Ubarte

et al. 2022

Sensors

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Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik’s wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation’s success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

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Section: Resultsmentioning

confidence: 99%

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Kaklauskas

Abraham

Ubarte

et al. 2022

Sensors

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“…A conventional objective in sentiment analysis involves determining whether the tone conveyed in a given text is positive, negative, or neutral. The advent of advanced language models like BERT and RoBERTa enables the exploration of more intricate data domains, such as texts where authors tend to express their opinions or sentiments less explicitly, or even rely on the use of emoticons (Ho et al 2020;Hamborg et al 2021). In this study, we employ the XLM-RoBERTa language model, developed by Facebook AI (Conneau et al 2019).…”

Section: Social Media Intensity and Sentimentmentioning

confidence: 99%

Do not shut up and do dribble: social media and TV consumption

Pazzona,

Spagnolo

2024

J Popul Econ

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This paper investigates the impact of social media interest and sentiment surrounding the 2020 National Basketball Association’s involvement with the Black Lives Matter movement on the television audience in the United States. Twitter (now known as X) serves as the chosen social media platform, and we determine the sentiment expressed in tweets (messages posted on Twitter) using the XLM-RoBERTa deep language model. Our primary findings indicate that the quantity of users’ posts does not significantly influence TV viewership; instead, the tone of the messages plays a crucial role. Positive messages supporting the NBA’s engagement correlate with an increase in the number of viewers, while those expressing opposition do not. We argue that this asymmetry may stem from a positive elasticity among casual (non-habitual) NBA viewers concerning positive sentiments toward NBA involvement. These viewers are likely to align with the NBA’s stances on civil rights and BLM. In contrast, the core NBA fan base exhibits inelastic demand and is unlikely to cease watching NBA games. A comprehensive set of robustness checks reinforces the validity of our key conclusions.

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“…The main task in sentiment analysis is to classify whether the opinion expressed in a particular text is positive, negative, or neutral. Further tasks include distinguishing emotions such as pleasure, disgust, sadness, anger, fear, or surprise [ 35 ]. Emotion is a complex psycho-physiological change that arises from the interaction of the individual's mood with biochemical and environmental influences.…”

Section: Introductionmentioning

confidence: 99%

The Scalable Fuzzy Inference-Based Ensemble Method for Sentiment Analysis

Isikdemir

Yavuz

2022

Computational Intelligence and Neuroscience

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Internet environments such as social networks, news sites, and blogs are the platforms where people can share their ideas and opinions. Many people share their comments instantly on the internet, which results in creating large volumes of entries. It is important for institutions and organizations to analyze this big data in an efficient and rapid manner to produce summary information about the feelings or opinions of individuals. In this study, we propose a scalable framework that makes sentiment classification by evaluating the compound probability scores of the most widely used methods in sentiment analysis through a fuzzy inference mechanism in an ensemble manner. The designed fuzzy inference system makes the sentiment estimation by evaluating the compound scores of valance aware dictionary, word embedding, and count vectorization processes. The difference of the proposed method from the classical ensemble methods is that it allows weighting of base learners and combines the strengths of each algorithm through fuzzy rules. The sentiment estimation process from text data can be managed either as a 2-class (positive and negative) or as a 3-class (positive, neutral, and negative) problem. We performed the experimental work on four available tagged social network data sets for both 2-class and 3-class classifications and observed that the proposed method provides improvements in accuracy.

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Emotion Recognition for Vietnamese Social Media Text

Cited by 20 publications

References 17 publications

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Do not shut up and do dribble: social media and TV consumption

The Scalable Fuzzy Inference-Based Ensemble Method for Sentiment Analysis

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