Abstract:
Sentiment analysis has kept its landmark in Natural Language Processing by analyzing the
text to extract sentimental value. The result usually goes with positive, negative, or neutral
sentiment with necessary data preprocessing, data processing, and encoding. We collected
the dataset from Kaggle which obtains the comments taken from Reddit posts regarding
the Israel-Palestine conflict. The previous works and hypotheses were analyzedand the
implementation of KNN and SVM is effective on those implementations. However, the
whole concept of sentiment analysis is broadly focused on Natural Language Processing,
and algorithms related to it should be used for actual accuracy and analysis. We applied
Sentiment Intensity Analyzer and TextBlob algorithms and libraries to do the sentiment
analysis of the desired dataset and compared them. These twoalgorithms have been widely
described until today for their efficiency in sentimentanalysis. We found accuracy of
87.74% and 49.08% in the Sentiment Intensity Analyzer and TextBlob algorithm
respectively. The best algorithm found here is the Sentiment Intensity Analyzer and we
tested it accordingly. Finally, we showed Geopolitical Stance by applying manually entered
input on topics, such as - Against Israel/Palestine, Supports Palestine, Neutral/Stance Not
Clear. These two algorithms are easy and time-saving whereas traditional machine
learning algorithm like KNN and SVM takes a lot of time and arealso not significant
for sentiment analysis.
