Sentiment Analysis on Global Warming Tweets Using Naïve Bayes and RNN

Joy, Deborah T.; Thada, Vikas; Srivastava, Utpal

doi:10.1007/978-981-16-2641-8_21

Cited by 5 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, a recent study by [34] presents a data processing pipeline combining traditional methods and deep learning, such as VADER, TextBlob, and LSTMs, specifically for environmental tweets. A similar pipeline approach involving Naïve Bayes and RNN was employed in a recent study on Sentiment Analysis on global warming-related tweets [35].…”

Section: ) Environmental Tweetsmentioning

confidence: 99%

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

Amangeldi,

Usmanova,

Shamoi

2024

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: ) Environmental Tweetsmentioning

confidence: 99%

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

Amangeldi,

Usmanova,

Shamoi

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Recurrent Neural Network (RNN) is a type of Neural Network where the outputs from the previous steps are fed as input to the current step ( Cho et al, 2014 ). It is an efficient model for sentiment analysis, there are several recent works for sentiment analysis using RNN, such as Balakrishnan et al, 2022 , Bonet, 2022 , Iwendi et al, 2022 , Joshi and Kanoongo, 2022 , Joy et al, 2022 , Oh et al, 2022 , Rodrigues et al, 2022 , Sany et al, 2022 , Singh et al, 2022 and Tran, Nguyen, and Dao (2022) , as it uses memory cells that are capable to capture information about long sequences. It has improved the time complexity rather than other models.…”

Section: Related Workmentioning

confidence: 99%