Purpose–Thispaperaimed to develop a system that applies VADER Sentiment Analysis to tweets collected using adevelopedtwitter scraper toolto identify the insights of public responsesbased on their tweetson certain government servicesrendered to them thus providing legislators of the province of Laguna an additional tool in writing future legislations.Method–This study may serve as an additional tool tothe Sangguniang Panlalawigan of Laguna in identifying sentiments of the public in terms of government services that are rendered and lack thereof based on the collected tweets written in Tagalog, English or Taglish(Tagalog and English).Data collected through the Twitter scraper tool are preprocessed taking into consideration the special characters that also have impact on scoring sentiments, emojis,and emoticons. The compound score is computed by normalizing the sum of the polarityscores foreach tweet.Results–Aside from a tabular visualization of VADER’s results, the system also provides graphical representation of the evaluation result with the percentage of positive neutral and negative tweets. Based on the result of the testing and evaluation, the VADER model is 80.71% accurate and had an F-score of 84.33%.Conclusion–The reports generated from the system be utilized to serve as potentially additional basis for legislators of the province of Laguna in writing legislations such as resolutions and ordinances based on the sentiment or voice of the community. Recommendations–It is recommended to collaborate with linguists to develop a native language of VADER’s lexicon to improve the accuracy of the sentiment scores.
Purpose -The legislative branch of a province is generally in charge of making laws. In addition to this, they are also in charge of enacting programs and policies for the general well-being of the citizens within the province. The general public may not be able to keep track of legislative performances since there is a growing number of legislative-generated documents. This study developed a system that integrated the application of topic modeling in crafting ordinances, resolutions, and policies for the Province of Laguna.Method -This research employed the SCRUM methodology process in the development of the system and Latent Dirichlet Allocation (LDA) topic modeling using R language to classify text within a document to a particular topic and created model through Observation-based evaluation and Quantitative metrics such as Perplexity and Coherence to determine k-value (number of topics) based on the corpus wherein it was collected in the Twitter and Legislative Management System Portal.Results -The results showed the LDA used returned the optimal value for perplexity and coherence which was determined by testing different k-values ranging from 1 to 2 which is presented to users as a line graph. The developed system provided a system module 1163 that can enable users to find the optimal number of topics (k value) and present the results in a visually appealing interface on the user's account portal which gives insights into what possible new ideas in formulation ordinances, resolutions, and policies in Laguna. Conclusion -The developed system in this study allows legislators of the province of Laguna to collect public posts from the social networking site Twitter and use Latent Dirichlet Allocation (LDA) topic modeling. It also provides an interactive graph that allows users to explore the LDA model generated by the system and helps to reveal topics of concern from the community that leads to government officials in formulating policies and ordinances appropriate for the needs of the community.Recommendations -It is recommended to develop an additional module that automatically generates the topic model based on the selected LDA evaluation procedure and should be tested in a larger-sized corpus to further test its capabilities as well as to improve the list of the stop words and noise removal feature. Implications -The system can be used to simply accomplish the document trail page where users can preview document details and the application of the visualization techniques in the system helps to facilitate the to provide an impression by extracting words, and topics that can be a basis of crafting programs and priorities of the government officials in taking actions to the citizen concerns.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.