OPINION MINING CHALLENGES AND CASE STUDY: USING TWITTER FOR SENTIMENT ANALYSIS TOWARDS PFIZER/BIONTECH, MODERNA, ASTRAZENECA/OXFORD, AND SPUTNIK COVID-19 VACCINES.

Abstract

The increasing number of users on the internet and online servers such as Twitter, Amazon, Yelp, and Facebook has significantly motivated people not only to use the internet for their transactions, but more importantly to voice their opinions about servers, products, policies, etc. Sentiment analysis is the task of classifying opinions about specific topics, such as servers, products, and policies into positive, negative, and neutral categories. The field of sentiment analysis was introduced about 20 years ago, and it has widespread applications and models in different domains such as marketing, risk management, and politics. Moreover, opinion mining has significant impacts on businesses, servers, politics, and other significant strands of society, making it important to areas that benefit several fields in real life. This research introduces various problems facing the opinion mining field relative to the Covid-19 vaccines and suggests the parameters of the application to illuminate the definition of the problem and its effect on the opinion mining field and application. Those problems include sentiment analysis accuracy, sentiment lexicon, natural language processing issues, fake opinions, subjectivity detection, and opinion summarization. Each problem contains an important variable in the content of the text that can have an important role in an opinion mining task model. The field of sentiment analysis has made major leaps in its application and field. However, this research presents shortcomings in the field of sentiment analysis regardless of the importance of the key aspects of this field. Included as well is a case study related to the sentiment analysis task using Twitter for sentiment analysis towards Pfizer/BioNTech, Moderna, AstraZeneca/Oxford, and Sputnik Covid-19 vaccines. Applying sentiment analysis can be done in many languages source codes such as C, Java, Python, etc. This research applies sentiment analysis using Python language source code, since it has a library that supports data analysis. The case study will include preprocessing, sentiment analysis, and visualization. The aim of this thesis is to introduce and illuminate the challenges facing the opinion mining (sentiment analysis) field.