@inbook{166a6f1f368e4542bde49392a47e0243,
title = "Advancing Sentiment Analysis of Social Media Data: Unveiling Public Perception of Environmental Challenges in Malaysia",
abstract = "This study proposes a novel method for sentiment analysis of social media data, specifically X (formerly known as Twitter) data, and demonstrates its efficacy for predicting sentiments related to environmental issues. The proposed method employed state-of-the-art algorithms and machine learning approaches to perform the sentiment analysis of posts pertaining to environmental challenges in Malaysia, a country with significant social media usage in the Asia-Pacific region. The results showed that most of the posts analyzed were neutral in sentiment, suggesting a less polarized discourse on environmental challenges in Malaysia. The findings highlight the need for policy changes and environmental education to promote concern for environmental challenges and pro-environmental behavior among Malaysian residents. The proposed method is simple to use and accurately predicted sentiment from the X data. In addition to providing a valuable tool for researchers, the method has the potential to advance the field of sentiment analysis of social media data and be replicated in future research and practice.",
keywords = "Big data analytics, Environmental challenges, Sentiment analysis, Social media, Text mining",
author = "Anum Zahra and Lan Ma and Khong, {Kok Wei}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.",
year = "2025",
doi = "10.1007/978-3-031-68952-9_21",
language = "English",
series = "Signals and Communication Technology",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "159--167",
booktitle = "Signals and Communication Technology",
address = "Germany",
}