Leveraging Social Media Data for Forest Fires Sentiment Classification: A Data-Driven Method
Downloads
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
A. Khorram-Manesh, K. Goniewicz, A. Hertelendy, and M. Dulebenets, Handbook of disaster and emergency management. Kompendiet, 2021.
M. P. Thompson et al., “Potential operational delineations: New horizons for proactive, risk-informed strategic land and fire management,” Fire Ecol., vol. 18, no. 1, 2022, doi: 10.1186/s42408-022-00139-2.
F.-N. Robinne and F. Secretariat, “Impacts of disasters on forests, in particular forest fires,” UNFFS Background paper, 2021.
J. A. Wang, J. T. Randerson, M. L. Goulden, C. A. Knight, and J. J. Battles, “Losses of Tree Cover in California Driven by Increasing Fire Disturbance and Climate Stress,” AGU Adv., vol. 3, no. 4,2022, Art.no. e2021AV000654, doi: 10.1029/2021AV000654.
J. MacCarthy, J. Richter, S. Tyukavina, M. Weisse, and N. Harris, “The latest data confirms: Forest fires are getting worse”. Accessed: August 29, 2023. [Online]. Available: https://www. wri.org/insights/global-trends-forest-fires
S. Thapa et al., “Forest fire detection and monitoring,” Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region: A Decade of Experience from SERVIR, pp. 147–167, 2021.
A. Nurkholis, D. Alita, A. Sucipto, M. Chanafy, Z. Amalia, and others, “Hotspot classification for forest fire prediction using c5. 0 algorithm,” in 2021 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), 2021, pp. 12–16.
R.S. Priya, K. Vani, “Deep learning based forest fire classification and detection in satellite images,” in 2019 11th International Conference on Advanced Computing (ICoAC), 2019, pp. 61–65, doi: 10.1109/ICoAC48765.2019.246817.
Y. Kang, E. Jang, J. Im, and C. Kwon, “A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency,” GIsci Remote Sens, vol. 59, no. 1, pp. 2019–2035, 2022, doi: 10.1080/15481603.2022.2143872.
B. T. Pham et al., “Performance evaluation of machine learning methods for forest fire modeling and prediction,” Symmetry, vol. 12, no. 6, p. 1022, 2020, doi: 10.3390/sym12061022.
T. Preeti, S. Kanakaraddi, A. Beelagi, S. Malagi, and A. Sudi, “Forest fire prediction using machine learning techniques,” in 2021 International Conference on Intelligent Technologies (CONIT), 2021, pp. 1–6, doi: 10.1109/CONIT51480.2021.9498448.
X. Wu, G. Zhang, Z. Yang, S. Tan, Y. Yang, and Z. Pang, “Machine learning for predicting forest fire occurrence in Changsha: An innovative investigation into the introduction of a forest fuel factor,” Remote Sens vol. 15, no. 17, p. 4208, 2023, doi: 10.3390/rs15174208.
S. Yang, M. Lupascu, and K. S. Meel, “Predicting forest fire using remote sensing data and machine learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 14983–14990, doi: 10.1609/aaai.v35i17.17758.
A. Singla and R. Agrawal, “Social media and disaster management: Investigating challenges and enablers,” Global Knowledge, Memory and Communication, vol. 73, no. 1/2, pp. 100–122, 2024, doi: 10.1108/GKMC-12-2021-0206.
H. Li, D. Caragea, C. Caragea, and N. Herndon, “Disaster response aided by tweet classification with a domain adaptation approach,” Journal of Contingencies and Crisis Management, vol. 26, no. 1, pp. 16–27, 2018, doi: 10.1111/1468-5973.12194.
P. M. Landwehr and K. M. Carley, “Social media in disaster relief,” in Data mining and knowledge discovery for big data, Springer, 2014, pp. 225–257, doi: 10.1007/978-3-642-40837-3_7.
R. I. Ogie, S. James, A. Moore, T. Dilworth, M. Amirghasemi, and J. Whittaker, “Social media use in disaster recovery: A systematic literature review,” International Journal of Disaster Risk Reduction, vol. 70, p. 102783, 2022, doi: 10.1016/j.ijdrr.2022.102783.
J. Lever, R. Arcucci, and J. Cai, “Social data assimilation of human sensor networks for wildfires,” in Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, 2022, pp. 455–462, doi: 10.1145/3529190.3534735.
J. Lever and R. Arcucci, “Towards social machine learning for natural disasters,” in International Conference on Computational Science, 2022, pp. 756–769, doi: 10.1007/978-3-031-08757-8_62.
J. Lever, S. Cheng, and R. Arcucci, “Human-sensors & physics aware machine learning for wildfire detection and nowcasting,” in International Conference on Computational Science, 2023, pp. 422–429, doi: 10.1007/978-3-031-36027-5_3.
J. Lever and R. Arcucci, “Sentimental wildfire: A social-physics machine learning model for wildfire nowcasting,” J Comput Soc Sci, vol. 5, no. 2, pp. 1427–1465, 2022, doi: 10.1007/s42001-022-00174-8.
C. Boulton, H. Shotton, and H. Williams, “Using social media to detect and locate wildfires,” in Proceedings of the International AAAI Conference on Web and Social Media, 2016, pp. 178–186, doi: 10.1609/icwsm.v10i2.14850.
G. Zamarreño-Aramendia, F. J. Cristòfol, J. de-San-Eugenio-Vela, and X. Ginesta, “Social-media analysis for disaster prevention: Forest fire in Artenara and Valleseco, Canary Islands,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 6, no. 4, p. 169, 2020, doi: 10.3390/joitmc6040169.
A. A. Fitriany, P. J. Flatau, K. Khoirunurrofik, and N. F. Riama, “Assessment on the use of meteorological and social media information for forest fire detection and prediction in Riau, Indonesia,” Sustainability, vol. 13, no. 20, p. 11188, 2021, doi: 10.3390/su132011188.
A. Rachman, B. H. Saharjo, and E. I. K. Putri, “Forest and land fire prevention strategies in the forest management Unit Kubu Raya, South Ketapang, and North Ketapang in West Kalimantan Province,” 2020.
BNPB, “Berbagi Informasi Real-time Situasi Darurat Melalui PetaBencana.id.”. Accessed: August 29, 2023. [Online]. Available: https://www.bnpb.go.id/berita/berbagi-informasi-realtime-situasi-darurat-melalui-petabencana-id-
S. Madichetty, “Classifying informative and non-informative tweets from the twitter by adapting image features during disaster,” Multimed Tools Appl, vol. 79, no. 39, pp. 28901–28923, 2020, doi: 10.1007/s11042-020-09343-1.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A benchmark dataset and pre-trained language model for Indonesian NLP,” arXiv preprint arXiv:2011.00677, 2020.
S. M. Davey and A. Sarre, “the 2019/20 Black Summer bushfires,” 2020, Taylor & Francis, doi: 10.1080/00049158.2020.1769899.
P. Brando et al., “Amazon wildfires: Scenes from a foreseeable disaster,” Flora, vol. 268, p. 151609, 2020, doi: 10.1016/j.flora.2020.151609.
G. Zittis et al., “Climate change and weather extremes in the Eastern Mediterranean and Middle East,” Reviews of Geophysics, vol. 60, no. 3, p. e2021RG000762, 2022, doi: 10.1029/2021RG000762.
Forest Watch Indonesia, “Tentang hutan indonesia.” Accessed: August 29, 2023. [Online]. Available: https://fwi.or.id/
A. V Mavrodieva and R. Shaw, “Social media in disaster management,” Media and Disaster Risk Reduction: Advances, Challenges and Potentials, pp. 55–73, 2021, doi: 10.1007/978-981-16-0285-6_4
L. Yan and A. J. Pedraza-Martinez, “Social media for disaster management: Operational value of the social conversation,” Prod Oper Manag, vol. 28, no. 10, pp. 2514–2532, 2019, doi: 10.1111/poms.13064.
Z. Wang and X. Ye, “Social media analytics for natural disaster management,” International Journal of Geographical Information Science, vol. 32, no. 1, pp. 49–72, 2018, doi: 10.1080/13658816.2017.1367003.
W. Budiharto and M. Meiliana, “Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis,” J Big Data, vol. 5, no. 1, pp. 1–10, 2018, doi: 10.1186/s40537-018-0164-1.
T. Mustaqim, K. Umam, and M. A. Muslim, “Twitter text mining for sentiment analysis on government’s response to forest fires with vader lexicon polarity detection and k-nearest neighbor algorithm,” in Journal of Physics: Conference Series, 2020, p. 32024, doi: 10.1088/1742-6596/1567/3/032024.
I. Irawanto, C. Widodo, A. Hasanah, P. A. D. Kusumah, K. Kusrini, and K. Kusnawi, “Sentiment analysis and classification of Forest Fires in Indonesia,” ILKOM Jurnal Ilmiah, vol. 15, no. 1, pp. 175–185, 2023, doi: 10.33096/ilkom.v15i1.1337.175-185.
B. S. Negara, R. Kurniawan, M. Z. A. Nazri, S. Abdullah, R. W. Saputra, and A. Ismanto, “Riau forest fire prediction using supervised machine learning,” in Journal of Physics: Conference Series, 2020, p. 12002, doi: 10.1088/1742-6596/1566/1/012002.
L. W. Evelina, M. Rizkiansyah, I. Prawira, R. E. Irawan, and others, “Comparative Forest Preservation Campaign Government and NGO on Indonesia Instagram,” in 2020 International Conference on Information Management and Technology (ICIMTech), 2020, pp. 370–375, doi: 10.1109/ICIMTech50083.2020.9211190.
S. Hasyim, R. Abdullah, and H. Ibrahim, “Forest damage and preservation through forest resources management in Indonesia,” GeoJournal, vol. 86, pp. 2183–2189, 2021, doi: 10.1007/s10708-020-10177-5.
M. Muslikhin, L. W. Evelina, M. Rizkiansyah, I. Prawira, and R. E. Irawan, “Environmental communication network on Indonesian forest fires on Twitter in pandemic COVID-19 era,” in IOP Conference Series: Earth and Environmental Science, 2022, p. 12026, doi: 10.1088/1755-1315/951/1/012026.
T. Nikonovas, A. Spessa, S. H. Doerr, G. D. Clay, and S. Mezbahuddin, “ProbFire: a probabilistic fire early warning system for Indonesia,” Natural Hazards and Earth System Sciences, vol. 22, no. 2, pp. 303–322, 2022, doi: 10.5194/nhess-22-303-2022.
M. Kibanov, G. Stumme, I. Amin, and J. G. Lee, “Mining social media to inform peatland fire and haze disaster management,” Soc Netw Anal Min, vol. 7, pp. 1–19, 2017, doi: 10.1007/s13278-017-0446-1.
V. Slavkovikj, S. Verstockt, S. Van Hoecke, and R. de Walle, “Review of wildfire detection using social media,” Fire Saf J, vol. 68, pp. 109–118, 2014, doi: 10.1016/j.firesaf.2014.05.021.
S. B. Kotsiantis, D. Kanellopoulos, and P. E. Pintelas, “Data preprocessing for supervised leaning,” Int J Comp Sci, vol. 1, no. 2, pp. 111–117, 2006.
L. Hickman, S. Thapa, L. Tay, M. Cao, and P. Srinivasan, “Text preprocessing for text mining in organizational research: Review and recommendations,” Organ Res Methods, vol. 25, no. 1, pp. 114–146, 2022, doi: 10.1177/1094428120971683.
A. Aizawa, “An information-theoretic perspective of tf-idf measures,” Inf Process Manag, vol. 39, no. 1, pp. 45–65, 2003, doi: 10.1016/S0306-4573(02)00021-3.
E. A. Rissola, D. E. Losada, and F. Crestani, “A survey of computational methods for online mental state assessment on social media,” ACM Trans Comput Healthc, vol. 2, no. 2, pp. 1–31, 2021, doi: 10.1145/3437259.
A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, and T. Mikolov, “Fasttext. zip: Compressing text classification models,” arXiv preprint arXiv:1612.03651, 2016.
F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: Continual prediction with LSTM,” Neural Comput, vol. 12, no. 10, pp. 2451–2471, 2000, doi: 10.1162/089976600300015015.
H. Abbasimehr, M. Shabani, and M. Yousefi, “An optimized model using LSTM network for demand forecasting,” Comput Ind Eng, vol. 143, p. 106435, 2020, doi: 10.1016/j.cie.2020.106435.
D. M. W. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” arXiv preprint arXiv:2010.16061, 2020.
B. CM, “Pattern Recognition and Machine Learning,‖ Springer,” 2006.
C. Hutto and E. Gilbert, “Vader: A parsimonious rule-based model for sentiment analysis of social media text,” in Proceedings of the international AAAI conference on web and social media, 2014, pp. 216–225, doi: 10.1609/icwsm.v8i1.14550.
A. Esuli and F. Sebastiani, “Sentiwordnet: A publicly available lexical resource for opinion mining,” in Proceedings of LREC, 2006, pp. 417–422.
L. Khan, A. Amjad, K. M. Afaq, and H.-T. Chang, “Deep sentiment analysis using CNN-LSTM architecture of English and Roman Urdu text shared in social media,” Applied Sciences, vol. 12, no. 5, p. 2694, 2022, doi: 10.3390/app12052694.
S. Madichetty and M. Sridevi, “A neural-based approach for detecting the situational information from Twitter during disaster,” IEEE Trans Comput Soc Syst, vol. 8, no. 4, pp. 870–880, 2021, doi: 10.1109/TCSS.2021.3064299.
K. Barik and S. Misra, “Analysis of customer reviews with an improved VADER lexicon classifier,” J Big Data, vol. 11, no. 1, p. 10, 2024, doi: 10.1186/s40537-023-00861-x.
P. Izsak, M. Berchansky, and O. Levy, “How to train BERT with an academic budget,” arXiv preprint arXiv:2104.07705, 2021.
K. L. Tan, C. P. Lee, K. S. M. Anbananthen, and K. M. Lim, “RoBERTa-LSTM: a hybrid model for sentiment analysis with transformer and recurrent neural network,” IEEE Access, vol. 10, pp. 21517–21525, 2022, doi: 10.1109/ACCESS.2022.3152828.
L. Zhang, S. Wang, and B. Liu, “Deep learning for sentiment analysis: A survey,” Wiley Interdiscip Rev Data Min Knowl Discov, vol. 8, no. 4, p. e1253, 2018, doi: 10.1002/widm.1253.
M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artif Intell Rev, vol. 55, no. 7, pp. 5731–5780, 2022, doi: 10.1007/s10462-022-10144-1.
M. Chiny, M. Chihab, O. Bencharef, and Y. Chihab, “LSTM, VADER and TF-IDF based hybrid sentiment analysis model,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 7, 2021, doi: 10.14569/IJACSA.2021.0120730.
Copyright (c) 2024 The Authors. Published by Universitas Airlangga.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
