ABSTRACT
The Covid-19 pandemic has hurt the business sector as badly as the health sector, including the logistics sector and delivery services. This sector is entering a period of uncertainty both in terms of government policies and demand which can affect the performance of the services provided. Evaluation needs to be done to see whether the logistics and shipping services sector, especially in Indonesia, is ready to face a pandemic. The availability of official service accounts of each service provider on Twitter social media as a forum for complaints and public aspirations can be used to evaluate service performance by measuring customer satisfaction levels through sentiment analysis before and during the pandemic. Various kinds of research on sentiment analysis have been carried out, but the time window sentiment analysis especially Time-Window Lexicon TF-IDF SVM integrating model approach has not been widely used. The data were obtained by scrapping the entire data for October 2019 until September 2020. ± 10.000 random stratified training samples data per month per service provider were taken and labeled using lexical approach for classification model creation The classification model was done with 89,01% accuracy, which is then deployed to predict the sentiment label of the whole data. This study provides the results that: (1) the Covid-19 pandemic significantly increase the number of tweet that indicate more people use the service, (2) the Covid-19 pandemic have decreased the performance of logistics and delivery services especially at the first three months of the pandemic period, and (3) the most frequent negative opinion that significantly affects service performance is late in delivery.
- Jean-François Arvis, Lauri Ojala, Christina Wiederer, Ben Shepherd, Anasuya Raj, Karlygash Dairabayeva, and Tuomas Kiiski. 2018. Connecting to Compete 2018. Connect. to Compete 2018 (2018). DOI:https://doi.org/10.1596/29971Google Scholar
- Dinda Ayu Widiastuti. 2020. Pengguna aktif harian Twitter meningkat pada Q1 2020. Retrieved February 16, 2021 from https://www.tek.id/tek/pengguna-aktif-harian-twitter-meningkat-pada-q1-2020-b1ZML9hMrGoogle Scholar
- Rupal Bhargava and Yashvardhan Sharma. 2017. MSATS: Multilingual sentiment analysis via text summarization. Proc. 7th Int. Conf. Conflu. 2017 Cloud Comput. Data Sci. Eng. (2017), 71–76. DOI:https://doi.org/10.1109/CONFLUENCE.2017.7943126Google ScholarCross Ref
- Erik Cambria, Soujanya Poria, Alexander Gelbukh, Instituto Politécnico Nacional, and Mike Thelwall. 2017. AFFECTIVE COMPUTING AND SENTIMENT ANALYSIS Sentiment Analysis Is a Big Suitcase. Ieee Intell. Syst. (2017).Google Scholar
- Wafaa S. El-Kassas, Cherif R. Salama, Ahmed A. Rafea, and Hoda K. Mohamed. 2021. Automatic text summarization: A comprehensive survey. Expert Syst. Appl. 165, (2021), 113679. DOI:https://doi.org/10.1016/j.eswa.2020.113679Google Scholar
- Amalia Nur Fitri and Noverius Laoli. 2020. Survei: Di masa pandemi, 85,2% masyarakat gunakan jasa kurir untuk pengiriman barang. Retrieved February 15, 2021 from https://industri.kontan.co.id/news/survei-di-masa-pandemi-852-masyarakat-gunakan-jasa-kurir-untuk-pengiriman-barangGoogle Scholar
- J Kazmaier and JH van Vuuren. 2020. Sentiment analysis of unstructured customer feedback for a retail bank. ORiON 36, 1 (2020), 35–71. DOI:https://doi.org/10.5784/36-1-668Google ScholarCross Ref
- Fajri Koto and Gemala Y. Rahmaningtyas. 2018. Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs. Proc. 2017 Int. Conf. Asian Lang. Process. IALP 2017 2018-Janua, (2018), 391–394. DOI:https://doi.org/10.1109/IALP.2017.8300625Google Scholar
- Bing Liu. 2015. Sentiment Analysis - Mining Opinions, Sentiments, and Emotions. Cambridge University Press, New York, USA.Google Scholar
- Annisa Marlin Masbar Rus, Rossi Annisa, Isti Surjandari, and Zulkarnain. 2019. Measuring hotel service quality in borobudur temple using opinion mining. 2019 16th Int. Conf. Serv. Syst. Serv. Manag. ICSSSM 2019 (2019), 1–5. DOI:https://doi.org/10.1109/ICSSSM.2019.8887650Google Scholar
- Hennie Tuhuteru and Ade Iriani. 2018. Analisis Sentimen Perusahaan Listrik Negara Cabang Ambon Menggunakan Metode Support Vector Machine dan Naive Bayes Classifier. J. Inform. J. Pengemb. IT 3, 3 (2018), 394–401. DOI:https://doi.org/10.30591/jpit.v3i3.977Google Scholar
- Clara Vania, Moh. Ibrahim, and Mirna Adriani. 2014. Sentiment Lexicon Generation for an Under-Resourced Language. Int. J. Comput. Linguist. Appl. 5, 1 (2014), 59–72.Google Scholar
- Mingyang Wang, Huan Wu, Tianyu Zhang, and Shengqing Zhu. 2020. Identifying critical outbreak time window of controversial events based on sentiment analysis. PLoS One 15, 10 October 2020 (2020), 1–20. DOI:https://doi.org/10.1371/journal.pone.0241355Google Scholar
- Ceshine Lee. 2018. Use TextRank to Extract Most Important Sentences in Article. Retrieved March 31, 2021 from https://medium.com/the-artificial-impostor/use-textrank-to-extract-most-importantsentences-in-article-b8efc7e70b4Google Scholar
- S. Vijayarani and S.Dhayanand. 2015. Data mining classification algorithms for kidney disease prediction. International Journal on Cybernetics Informatics 4 (4), 13-25.Google ScholarCross Ref
- Marsha L. Richins. 1984. Word of Mouth Communication As Negative Information. Advances in Consumer Research Volume 11, 697-702Google Scholar
- Tushar Rao and Saket Srivastava. 2012. Analyzing stock market movements using Twitter sentiment analysis. Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), 119-123.Google ScholarDigital Library
- SoYeop Yoo, JeIn Song, and OkRan Jeong. 2018. Social media contents based sentiment analysis and prediction system. Expert Systems with Applications Volume 105, 102-111.Google ScholarCross Ref
Index Terms
- Logistics and Domestic Delivery Services Performance in Covid-19 Era: A Sentiment Analysis Approach
Recommendations
ICT-enabled delivery of maternal health services
ICEGOV '12: Proceedings of the 6th International Conference on Theory and Practice of Electronic GovernanceFresh opportunities are being created daily for the deployment of Information and Communication Technologies (ICT) [42] particularly in the area of poverty alleviation and sustainable economic growth in developing countries in particular. Therefore, the ...
Joint sentiment/topic model for sentiment analysis
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementSentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet ...
Topic sentiment change analysis
MLDM'11: Proceedings of the 7th international conference on Machine learning and data mining in pattern recognitionPublic opinions on a topic may change over time. Topic Sentiment change analysis is a new research problem consisting of two main components: (a) mining opinions on a certain topic, and (b) detect significant changes of sentiment of the opinions on the ...
Comments