Modeling Label Semantics for Predicting Emotional Reactions

Gaonkar, Radhika; Kwon, Hee Young; Bastan, Mohaddeseh; Balasubramanian, Niranjan; Chambers, Nathanael

doi:10.18653/v1/2020.acl-main.426

Cited by 19 publications

(4 citation statements)

References 11 publications

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“…As proposed by Abdar et al [1], publi c preferences and attitudes on renewable energy can be gauged with multiple dimensions, including valence (positive versus negative) and arousal (high versu s low level of activation). Future research should aim to capture multidimensional emotion s, for example, by utilizing Sen-tiBert to capture compositional sentiment semantics [76], modeling irony and sarcasm detection [5 l ], and/or explicitly modeling label semantics in guiding an encoder network [18].…”

Section: Discussionmentioning

confidence: 99%

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

et al. 2021

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Public acceptance and support for renewable energy are important determinants of the low-carbon energy transition. This paper examines public sentiment toward solar energy in the United States using data from Twitter, a micro-blogging platform on which people post messages, known as tweets. We filtered tweets specific to solar energy and performed a classification task using Robustly optimized Bidirectional Encoder Representations from Transformers (RoBERTa). Our RoBERTa-based sentiment classification model, fine-tuned with 6300 manually annotated tweets specific to solar energy, attains 80.2% accuracy for ternary (positive, neutral, or negative) classification. Analyzing 266,686 tweets during the period of January to December 2020, we find public sentiment varies widely across states (Coefficient of Variation =164.66%). Within the study period, the Northeast U.S. region shows more positive sentiment toward solar energy than did the South U.S. region. Public opinion on solar energy is more positive in states with a larger share of Democratic voters in the 2020 presidential election. Public sentiment toward solar energy is more positive in states with consumer-friendly net metering policies and a more mature solar market. States that wish to gain public support for solar energy might want to consider implementing consumer-friendly net metering policies and support the growth of solar businesses.

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Section: Discussionmentioning

confidence: 99%

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

et al. 2021

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“…Using approaches like the cosine similarity method, the latent semantic space can be further condensed to locate similar words and documents. Neuropsychological, phrase comprehension reviewer choice and research article recommendation (LSA model) semantic categorization clustering of words (Bakhshi et al, 2020;Bernard et al, 2020;Christy et al, 2020;Gaonkar, 2019). On the bsite2, you may view examples of LSA in action.…”

Section: Latent Semantic Analysis (Lsa)mentioning

confidence: 99%

Deep Learning Speech Synthesis Model for Word/Character-Level Recognition in the Tamil Language

Rajendran

Raja

Nagarajan³

et al. 2023

International Journal of E-Collaboration

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As electronics and the increasing popularity of social media are widely used, a large amount of text data is created at unprecedented rates. All data created cannot be read by humans, and what they discuss in their sphere of interest may be found. Modeling of themes is a way to identify subjects in a vast number of texts. There has been a lot of study on subject-modeling in English. At the same time, millions of people worldwide speak Tamil; there is no great development in resource-scarce languages such as Tamil being spoken by millions of people worldwide. The consequences of specific deep learning models are usually difficult to interpret for the typical user. They are utilizing various visualization techniques to represent the outcomes of deep learning in a meaningful way. Then, they use metrics like similarity, correlation, perplexity, and coherence to evaluate the deep learning models.

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“…[48] included four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) in their framework to facilitate zero-shot text classification. [49] explicitly modeled label embeddings and their mutual correlations in emotion inference for ROC stories [50]. Related to emotion recognition is the task of Aspect-based sentiment analysis (ABSA) which entails the identification of opinion polarity towards a specific aspect in a given comment.…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Emotion Prediction by Modeling Emotion Definitions

Singh¹,

Brahma²,

Rai³

et al. 2021

Preprint

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In this paper, we propose a new framework for fine-grained emotion prediction in the text through emotion definition modeling. Our approach involves a multi-task learning framework that models definitions of emotions as an auxiliary task while being trained on the primary task of emotion prediction. We model definitions using masked language modeling and class definition prediction tasks. Our models outperform existing state-of-the-art for fine-grained emotion dataset GoEmotions. We further show that this trained model can be used for transfer learning on other benchmark datasets in emotion prediction with varying emotion label sets, domains, and sizes. The proposed models outperform the baselines on transfer learning experiments demonstrating the generalization capability of the models.

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Modeling Label Semantics for Predicting Emotional Reactions

Cited by 19 publications

References 11 publications

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

Deep Learning Speech Synthesis Model for Word/Character-Level Recognition in the Tamil Language

Fine-Grained Emotion Prediction by Modeling Emotion Definitions

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