5 research outputs found
Sustainable governance in smart cities and use of supervised learning based opinion mining
Evaluation is an analytical and organized process to figure out the present positive influences, favourable future prospects, existing shortcomings and ulterior complexities of any plan, program, practice or a policy. Evaluation of policy is an essential and vital process required to measure the performance or progression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched by government in view of citizen welfare. Although, the governmental policies intend to better shape up the life quality of people but may also impact their every day’s life. A latest governmental scheme Saubhagya launched by Indian government in 2017 has been selected for evaluation by applying opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. The primary intent is to offer opinion mining as a smart city technology that harness the user-generated big data and analyse it to offer a sustainable governance model
Sentiment Analysis of Text Memes: A Comparison Among Supervised Machine Learning Methods
Meme is a new form of content in social media. A meme contains sentiment towards a particular issue, product, person, or entity. Memes can be in the form of text, images, or images that contain text. Memes are entertaining, critical, sarcastic, and may even be political. Traditional sentiment analysis methods deal with text. This study compares the performance of four sentiment analysis methods when used on Indonesian meme in the form of text and images that contain text. Firstly, the extraction of text memes was carried out, followed by the classification of the extracted text memes using supervised machine learning methods, namely NaĂŻve Bayes, Support Vector Machines, Decision Tree, and Convolutional Neural Networks. Based on the experimental results, sentiment analysis on meme text using the NaĂŻve Bayes method produced the best results, with an accuracy of 65.4%
Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning
Sustainable development plays a vital role in information and communication technology.
In times of pandemics such as COVID-19, vulnerable people need help to survive. This help
includes the distribution of relief packages and materials by the government with the primary
objective of lessening the economic and psychological effects on the citizens affected by disasters
such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public
funds’ accountability and transparency, especially in developing countries such as Nigeria. The
understanding of public emotions by the government on distributed palliatives is important as it
would indicate the reach and impact of the distribution exercise. Although several studies on English
emotion classification have been conducted, these studies are not portable to a wider inclusive
Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak,
has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion
classification of Standard English machine learning models. An Informal Nigerian English (Pidgin
English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used
to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives
and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML
algorithms are used in this study, and a comparative analysis of their performance is conducted. The
algorithms are Multinomial NaĂŻve Bayes (MNB), Support Vector Machine (SVM), Random Forest
(RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted
experiments reveal that Support Vector Machine outperforms the remaining classifiers with the
highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness,
joy, fear, and anger, with the highest number of counts from the classification conducted on the
constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship
between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the
palliatives’ distribution but afraid of inequality and transparency in the distribution process due to
reasons such as corruption. Conclusively, the results from this experiment clearly show that the public
emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory,
considering that the negative emotions from the public outnumbered the public happiness
Sentiment Analysis in Digital Spaces: An Overview of Reviews
Sentiment analysis (SA) is commonly applied to digital textual data,
revealing insight into opinions and feelings. Many systematic reviews have
summarized existing work, but often overlook discussions of validity and
scientific practices. Here, we present an overview of reviews, synthesizing 38
systematic reviews, containing 2,275 primary studies. We devise a bespoke
quality assessment framework designed to assess the rigor and quality of
systematic review methodologies and reporting standards. Our findings show
diverse applications and methods, limited reporting rigor, and challenges over
time. We discuss how future research and practitioners can address these issues
and highlight their importance across numerous applications.Comment: 44 pages, 4 figures, 6 tables, 3 appendice