Sentiment Analysis of Twitter Data on the Tourism Industry During the Covid-19 Pandemic

In most countries, the tourist sector becomes one of the significant factors for economic change. When it comes to Sri Lanka, the country's economy has grown primarily due to the tourism sector. Recently, the Covid-19 pandemic phase has had a significant negative impact on Sri Lanka's tourism industry. If we could recognize these problems, we would be able to prepare the tourism sector for future pandemics and ensure its stability. Studying peoples' opinions is crucial for taking the necessary measures, and Twitter is one of the greatest places to do this. This study suggests a model for categorizing tweets about the tourism sector during Covid-19 in Sri Lanka into four categories: positive, negative, commercial, and neutral. For this study, take into account 18980 tweets that were gathered on Twitter between the 2020 and 2022 time period. After selecting the 6257 tweets by prepossessing, the study extracts the feature vectors and result compares them using three different machine learning algorithms, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN). These results in a forecast paradigm for sentiment analysis of the movement of the tourism industry. To build a model, 67% of training data sets and 33% of testing data sets were used. The findings showed that LSTM, ANN and SVM accuracy was 85%, 91%, and 76% respectively. Additionally, the study takes into account the evaluation results of precision, recall, f-measure, and error values. The ANN algorithm provides the greatest results for sentiment analysis for monitoring Sri Lanka's tourism industry movements, according to the final results.