Abstract:
Covid-19 has caused havoc in people's lives in all nations and communities
and had a detrimental effect on the expansion of the world economy. Due to
the pandemic, political factors are also changed and it was urgent to examine
public opinion on political changes during Covid-19. Those opinions will help
to stabilize the countries in the future during these kind of pandemic situations.
The posts and comments on Twitter related to political opinions had posted
frequently. The study used Twitter to examine user opinions regarding the
political changes that occurred during the pandemic period. A sample of 10658
English tweets from the whole world was used during the period of Covid-19
from 2020 to 2022 and these tweets were gathered using the Twitter API. After
the tweets had been pre-processed, the feature vectors were produced using
the Term Frequency-Inverse Document Frequency (TF-IDF). To build a
forecast paradigm for sentiment analysis, the dataset was then put into
machine learning and deep learning algorithms such as Support Vector
Machine (SVM), Multilayer Perceptron (MLP), and Long Short-Term
Memory (LSTM). The results indicate, MLP performed better than SVM and
LSTM and had greater accuracy with 95.32% and also better recall, precision,
f-measure values, and lower error values. A training dataset of 77% was used
for the experiments. These algorithms classify tweets into four categories such
as positive, negative, neutral, and suggestions to the government. Based on the
results, we can estimate human opinions about political changes and can
address pertinent concerns as soon as possible.
