Figure 5 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Sentiment analysis results
Source publication
The use of restricted face-to-face learning during the epidemic in Indonesia was discussed not just by education and health professionals, but also on social media. The study used the Twitter dataset with the keywords 'school' and 'face-to-face' to examine public opinion about face-to-face learning. The research data was obtained from Twitter utili...
Similar publications
Most Korean (South Korean) cultural products, such as drama films, songs, fashion,and food, are rising worldwide. The Korean Wave or Hallyu phenomenon shows howKorean culture is accepted by people all over the world. Social media has piqued theinterest of Indonesians, particularly young people, in Korean culture. The phenomenoncreates new business,...
Online lecture is an alternative learning method during the Covid-19 pandemic. There are opinions with pro and contra of the learning method. The purpose of this study is to evaluate the tweets of opinion or sentiment retrieved from social media Twitter regarding online lectures among the Indonesian community. Twint is used to collect the data twee...
Cases of the spread of COVID-19 that continue to increase in Indonesia have made the level of public satisfaction with the government in dealing with this virus fairly low. One way to measure the level of community satisfaction is by analyzing social media. Sentiment analysis can be used to analyze feedback from the public. Research related to sent...
This research analyzes the sentiment of Twitter users regarding tourism in Indonesia using the keyword "wonderful Indonesia" as the tourism promotion identity. This study aims to gain a deeper understanding of the public sentiment towards "wonderful Indonesia" through social media data analysis. The novelty obtained provides new insights into valua...
Twitter is a popular social media platform that allows users to share short message’s opinion and engage in real-time conversations on a wide range of topics known as tweet. However, tweets often have a complicated and unclear context, which makes it difficult to determine the actual emotion. Therefore, sentiment analysis is required to see the ten...
Citations
... Artinya, guru harus mampu beradaptasi dengan cepat untuk menyiapkan pembelajaran daring yang efektif dan efisien, apapun tantangan atau masalah yang mereka hadapi. Selama pandemi, banyak penelitian yang dilakukan pada pembelajaran Bahasa dan Sastra Indonesia (Batubara et al., 2021). Studi menemukan bahwa masalah utamanya adalah siswa tidak memiliki semua sarana dan prasarana yang mereka butuh kan untuk melakukan kegiatan pembelajaran online, seperti jaringan internet dan perangkat teknologi (Prestiadi, 2020) . ...
Pandemi menyebabkan perubahan signifikan dalam pendidikan, termasuk pembelajaran online dan kebijakan belajar dari rumah. Penelitian ini mencakup pembelajaran daring pada dosen di Fakultas Keguruan dan Ilmu Kependidikan khususnya Program Studi Bahasa dan Sastra Indonesia Universitas Al Washliyah. Berdasarkan enam standar kinerja yaitu persiapan belajar, kegiatan pembelajaran, penilaian pembelajaran, interaksi interpersonal, pertumbuhan profesional, dan pengembangan sekolah, 25 dosen diminta mengisi kuesioner penilaian diri. Hasil penelitian menunjukkan bahwa dosen berada di level 3 untuk persiapan belajar, evaluasi pembelajaran, dan interaksi interpersonal. Dosen juga berada di level 2 untuk kegiatan pembelajaran, pengembangan profesi, dan standar pengembangan institusi. Hasil ini mengungkapkan pengetahuan berharga untuk mengambil inisiatif tindak lanjut yang dapat membantu dosen meningkatkan kinerja mereka
... Sentiment analysis can help to solve these issues. Sentiment analysis, also known as opinion mining, is the study of assigning views or emotional labels to texts in order to determine if the text reflects positive or negative opinions [1][2] [3]. ...
A film review is a subjective opinion of someone who has different feelings about each film. As a result, film enthusiasts will struggle to assess whether the film meets their requirements. Based on these issues, sentiment analysis is the best way to fix them. Sentiment analysis, also known as opinion mining, is the study of assigning views or emotional labels to texts in order to determine if the text contains positive or negative thoughts. The Nave Bayes method was chosen because it can classify data based on the computation of each class's probability against objects in a given data sample. The best model was created utilizing data without lemmatization, 500 vector sizes, and Nave Bayes classification, with an accuracy of 78.96 percent and a f1-score of 78.81 percent. Changes in vector size affect the system's capacity to foresee positive and negative sentiments. The difference in accuracy and recall values shows that when vector size 300 is utilized, the precision and recall outcomes are lower than when vector size 500 is used.
... The study used the polarity, subjectivity, and emotion of the tweets and concluded that the tweets were more negatively relevant to online education. Batubara et al. (2021) also made use of the Twitter data to perform sentiment analysis to detect the feasibility of face-to-face education during the COVID19 pandemic in Indonesia using tweets from many countries. ...
Online mode of education has been identified as the subtle solution to continue learning during the pandemic. However, the accessibility to online platforms, suitable devices, and connections are not equal across the globe thus raising the question of whether the opinion of the public in the South Asian region where the technology is not comparatively higher as in the western world would be the same as that to the global perspective. This study involves the sentiment analysis of natural language processing on recently tweeted data and concludes that the sentiment of the South Asian public remains positive as online education is the most suitable approach to overcome the learning difficulties during a pandemic. The study performs a ternary classification based on the polarity scores obtained from two robust lexicon-based sentiment analyzer tools namely VADER and TextBlob and observes that 63.2% of the tweets were positive, 30.5% of the tweets were neutral and around 6.3% of them were negative. Finally, topic modeling was also performed using the Latent Dirichlet Allocation method to gain insight into each of the classes.
