Sentiment analysis of tweets and government translations: Assessing China’s post-COVID-19 landscape for signs of withering or booming

Wang, Huan; Wang, Xiaohui

doi:10.1177/20594364231181745

Cited by 5 publications

(3 citation statements)

References 58 publications

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“…Moreover, it applies to air traffic control, rapid response medical teams, finance, data analytics, forensic science, archeological research, competitive sports, and musical performance. Each field demands precision and real-time assimilation of vast information, facilitating the development of nuanced skills aligned with real-world applications (Lowell & Moore, 2020;Wang & Wang, 2023). This approach aligns with the industry's goal of producing interpreters' adept at handling the challenges inherent in different interpreting scenarios.…”

Section: Expertise Differences In Interpreting Accuracymentioning

confidence: 95%

Expertise differences in cognitive interpreting: A meta‐analysis of eye tracking studies across four decades

Wang,

Yu,

Wang

2023

WIRES Cognitive Science

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This meta‐analytic research delves into the influence of expertise on cognitive interpreting, emphasizing time efficiency, accuracy, and cognitive effort, in alignment with prevailing expertise theories that link professional development and cognitive efficiency. The study assimilates empirical data from 18 eye‐tracking studies conducted over the past four decades, encompassing a sample of 1581 interpreters. The objective is to elucidate the role of expertise in interpretative performance while tracing the evolution of these dynamics over time. Findings suggest that expert interpreters outperform novices in time efficiency and accuracy and exhibit lower cognitive effort, especially in sight and consecutive interpreting. This effect is particularly pronounced in the English‐Chinese language pair and with the use of E‐prime and Tobii eye‐tracking systems. Further, fixation count and pupil size are essential metrics impacting cognitive effort. These findings have vital implications for interpreter training programs, suggesting a focus on expertise development to enhance efficiency and accuracy, reduce cognitive load, and emphasize the importance of sight interpreting as a foundational skill. The selection of technology and understanding of specific ocular metrics also emerged as essential for future research and practical applications in the interpreting industry.This article is categorized under: Psychology > Theory and Methods Linguistics > Cognitive

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Section: Expertise Differences In Interpreting Accuracymentioning

confidence: 95%

Expertise differences in cognitive interpreting: A meta‐analysis of eye tracking studies across four decades

Wang,

Yu,

Wang

2023

WIRES Cognitive Science

Self Cite

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“…This study contributes by evaluating the performance of these algorithms in comparison to traditional frequency-based text representation (TF-IDF) and prediction-based text representation (W2V) Experimental analysis was conducted on datasets including IMDB, Yelp, and tweets that were collected and labeled by researchers based on their sentiments. The results indicated that the model created using W2V and ANN demonstrated superior performance compared to other approaches (1)(2)(3)(4)19) .…”

Section: Classification Algorithmsmentioning

confidence: 96%

Sentiment Analysis Framework of Social Media Text by Feature Extraction and Machine Learning Model

Mathur¹,

Jain²,

Gupta³

et al. 2023

IJST

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Objectives: This research paper aims to analyze sentiment and opinions in online resources like discussion forums, review sites, and blogs. It also compares the effectiveness of three feature extraction techniques (TF-IDF, Word2Vec, and WAM) and evaluates three machine learning algorithms (Naïve Bayes, SVM, and ANN) for sentiment classification to determine the most accurate algorithm. Methods: The study utilizes sentiment-rich datasets from IMDB movie reviews, Yelp reviews, and tweets. Three feature extraction techniques are applied to extract relevant features and patterns from the text. Three machine learning algorithms are implemented to classify sentiments into positive, negative, and neutral categories. Accuracy, precision, recall, and F-measure are used to assess algorithm performance. The model is updated and refined three times to ensure reliability. Findings: The Artificial Neural Network (ANN) algorithm outperforms Naïve Bayes and Support Vector Machines, achieving an impressive accuracy rate of 99.74% for sentiment classification. Precision, recall, and F-measure exceed 98.5% after model refinement, demonstrating the approach's robustness. The study highlights the potential of sentiment analysis in online resources and emphasizes the ANN's superior accuracy, providing valuable insights for future sentiment analysis studies. Novelty: This research combines three popular feature extraction techniques in sentiment analysis, compares three machine learning algorithms on multiple datasets, and achieves a remarkable accuracy rate of 99.74% with the ANN. The study demonstrates the robustness of the approach through model refinement and contributes insights into sentiment analysis in online resources.

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“…Sentiment analysis algorithms empower researchers to assess the emotional tone and sentiment conveyed within religious texts (Wang and Wang 2023). By automatically classifying text segments as positive, negative, or neutral, sentiment analysis sheds light on the affective dimensions of religious texts (Yusof et al 2015).…”

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confidence: 99%

The Role of Artificial Intelligence in the Study of the Psychology of Religion

Alkhouri

2024

Religions

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The study of the psychology of religion encompasses various aspects of human experiences and beliefs, including the influence of emerging technologies such as artificial intelligence (AI). This article aims to examine the impact of AI on religious practices and rituals, highlighting its potential to reshape how individuals engage with spirituality. By exploring AI-powered religious applications, virtual communities, and online services, we seek to understand the transformation of traditional religious practices and raise important questions about authenticity, inclusiveness, and the role of technology in the psychology of religious contexts. Moreover, ethical considerations and challenges arising from the integration of AI into religion will be addressed. As researchers delve into this intersection, it is crucial to strike a balance between technological advancements and preserving the fundamental aspects of spirituality, personal growth, and genuine human connection. This article contributes to the existing literature by shedding light on the potential implications of AI in the realm of religious experiences, calling for further exploration of its ethical dimensions and unintended consequences. Ultimately, understanding the influence of AI on the psychology of religion prompts us to reflect on the nature of spirituality, belief formation, and the human experience itself.

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Sentiment analysis of tweets and government translations: Assessing China’s post-COVID-19 landscape for signs of withering or booming

Cited by 5 publications

References 58 publications

Expertise differences in cognitive interpreting: A meta‐analysis of eye tracking studies across four decades

Expertise differences in cognitive interpreting: A meta‐analysis of eye tracking studies across four decades

Sentiment Analysis Framework of Social Media Text by Feature Extraction and Machine Learning Model

The Role of Artificial Intelligence in the Study of the Psychology of Religion

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