China’s Public Firms’ Attitudes towards Environmental Protection Based on Sentiment Analysis and Random Forest Models

Li, Cai; Li, Luyu; Zheng, Jiaqi; Wang, Jizhi; Lv, Zezhong; Wei, Yinghao; Han, Qihang; Gao, Jiatong; Liu, Wenhao

doi:10.3390/su14095046

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…Recent studies like [30] and [33] indicate that the analysis of sentiments in Twitter data can effectively reveal public attitudes towards environmental actions. This is demonstrated by concerns surrounding field experiments, including potential adverse environmental impacts, the risk of progressing towards extensive implementation, and apprehension surrounding poor governance or even the illegality of such projects.…”

Section: ) Environmental Tweetsmentioning

confidence: 99%

Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data

Amangeldi,

Usmanova,

Shamoi

2024

IEEE Access

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Social media is now the predominant source of information due to the availability of immediate public response. As a result, social media data has become a valuable resource for comprehending public sentiments.Studies have shown that it can amplify ideas and influence public sentiments. This study analyzes the public perception of climate change and the environment over a decade from 2014 to 2023. Using the Pointwise Mutual Information (PMI) algorithm, we identify sentiment and explore prevailing emotions expressed within environmental tweets across various social media platforms, namely Twitter, Reddit, and YouTube. Accuracy on a human-annotated dataset was 0.65, higher than Vader's score but lower than that of an expert rater (0.90). Our findings suggest that negative environmental tweets are far more common than positive or neutral ones. Climate change, air quality, emissions, plastic, and recycling are the most discussed topics on all social media platforms, highlighting its huge global concern. The most common emotions in environmental tweets are fear, trust, and anticipation, demonstrating the wide and complex nature of public reactions. By identifying patterns and trends in opinions related to the environment, we hope to provide insights that can help raise awareness regarding environmental issues, inform the development of interventions, and adapt further actions to meet environmental challenges.

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Section: ) Environmental Tweetsmentioning

confidence: 99%

Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data

Amangeldi,

Usmanova,

Shamoi

2024

IEEE Access

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“…This method solved the heterogeneous effect of multiple intensifiers, increased the effectiveness of existing dictionaries in document polarity recognition, and took the relationship between emotion words and multiple intensifiers into account. Li et al [19] investigated changes in Chinese public firms' attitudes toward environmental protection between 2018 and 2021 by using a sentiment analysis method based on a sentiment dictionary. The study examined the relationship between firms' carbon reduction attitudes and financial performance, finding that specific policies can increase positive attitudes toward carbon reduction, and attitudes toward ecological topics differ across industries.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The calculation formulas are shown in Formulas ( 16)- (19). TP (True Positive): refers to the number of positive samples that are correctly classified as positive by the model; FP (False Positive): refers to the number of negative samples that are incorrectly classified as positive by the model; TN (True Negative): refers to the number of negative samples that are correctly classified as negative by the model; FN (False Negative): refers to the number of positive samples that are incorrectly classified as negative by the model.…”

Section: Performance Metricsmentioning

confidence: 99%

AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews

Liu

Wang

2023

Applied Sciences

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With the rapid development of the Internet, agricultural products have entered e-commerce platforms, and agricultural product reviews have become an important reference for consumers when purchasing agricultural products. However, due to the characteristics of different lengths, rich context-sensitive information, and multi-level information in the sentences of agricultural product reviews, the existing sentiment analysis methods cannot perform well enough to identify the sentiment tendency. To address this issue, we abstract the problem as a binary classification task to extract consumers’ sentiment orientation by proposing a new method. This method utilizes an attention mechanism to assign different weights to different key information in the sentence, thereby extracting abundant semantic information from the sentence. The design of the long short-term memory (LSTM) gate can effectively solve the problem of extracting long sequences and context-related information. The proposed model achieves superior results on two agricultural product datasets compared to other baseline models, providing guidance for merchants to improve agricultural product quality and enhance customer satisfaction.

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“…Finally, K automatic sample sets are generated after the above steps are repeated K times. Each automatic sample set is classified to form k decision trees, namely a random forest [48,49]. The specific process is shown in Figure 3.…”

Section: Random Forest Modelmentioning

confidence: 99%

Evaluation and Countermeasures of High-Quality Development of China’s Marine Economy Based on PSO-SVM

et al. 2022

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An accurate grasp of the high-quality development of the marine economy is important for the timely adjustment of marine policies and the promotion of sustainable development of the ocean. Based on the latest development philosophy, this paper constructed the evaluation index system of high-quality development of marine economy from five dimensions including innovation, coordination, green, openness, and sharing. The particle swarm optimization (PSO) algorithm and support vector machine (SVM) model based on the entropy weight composite index were employed to evaluate the high-quality development of China’s marine economy from 2006 to 2017. The spatial and temporal distribution characteristics and dynamic evolution mechanism were revealed. The random forest model was applied to analyze the main driving factors of high-quality development of the marine economy. It was found that: (1) The high-quality development level of marine economy in Guangdong, Shandong, Jiangsu, Zhejiang, and Shanghai has always been in the forefront. The growth rate of high-quality development level of marine economy in Guangdong and Shandong was 53.69% and 37.69%, respectively. The growth rates of Fujian and Hainan were 43.46% and 33.68%, respectively. Jiangsu and Zhejiang accounted for 33.30% and 24.47%, respectively. (2) The regulation methods of the main driving factors were examined. It was necessary to adhere to innovative development and improve the marine scientific research, education, management, and service industry, in addition by optimizing and adjusting the marine industrial infrastructure and spatial layout. It is also critical to strengthen the comprehensive prevention and control of land and sea pollution and implement the total emission control of pollutants into the sea. (3) Finally, the pathway for high-quality development of marine economy was analyzed and future directions were proposed.

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China’s Public Firms’ Attitudes towards Environmental Protection Based on Sentiment Analysis and Random Forest Models

Cited by 9 publications

References 16 publications

Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data

Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data

AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews

Evaluation and Countermeasures of High-Quality Development of China’s Marine Economy Based on PSO-SVM

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