Analyzing Public Concern Responsesfor Formulating Ordinances and Lawsusing Sentiment Analysis through VADER Application

Abstract

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.

Voices of the Stacks: A Multifaceted Inquiry into Academic Librarians' Tweets

“…VADER is an improvement upon earlier lexicon‐based methods of assessing sentiments, such as Linguistic Enquiry and Word count (LIWC), in that it incorporates a set of rules regarding word sequence, negation, punctuation, and other factors that are commonly used to add emphasis or otherwise augment or diminish the sentiment of phrases. It is also popular among information and social science researchers (Bose et al, 2021; Cruz & Balahadia, 2022; Pano & Kashef, 2020).…”