Leveraging Social Media Data for Forest Fires Sentiment Classification: A Data-Driven Method

Background: The rise in forest fires over the last two years, which is due to rise in dry weather conditions and human activities, have greatly impacted an area of 1.6 million hectares, leading to significant ecological, economic, and health issues, hence the need to improve disaster response strategies. Previous research determined the lack of coverage regarding public response during forest fires with conventional methods such as satellite images and sensor data. However, social media platforms provide real-time information generated by users, along with location information of disaster events. Sentiment analysis helps in understanding the public reactions and responses to natural disasters, thereby increasing awareness about forest fires. Objective: The purpose of this research is to assess the efficiency of Long Short-Term Memory (LSTM) method in classifying sentiment for social networks in regard to forest fires. This research aims to examine the effect of TF-IDF, unigram, and the FastText features on the effectiveness of the classification of sentiment. Methods: The precision, recall, and F1 score of 2, 3, and 4 determined in the LSTM models with commonly available sentiment analysis tools, such as the Vader Sentiment Analysis and SentiWordNet was used to evaluate the performance of the model. Results: With an improvement of roughly 10%, the four layers of the LSTM model generated the best performance for the evaluation of sentiments about forest fires. The LSTM model with FastText achieved F1, recall and precision scores of 0.649, 0.641, and 0.659, which exceeds lexicon-based method including SentiWordNet and Vader. Conclusion: The experimental results showed that the LSTM model outperformed lexicon-based methods when used to analyse the tweets related to forest fire. Additional research is required to integrate rule-based models and LSTM models to develop a more robust model for dynamic data. Keywords: Forest Fire, Disaster, Long Short-Term Memory, LSTM, Vader, SentiWordnet