Sentiment Analysis Based on Travelers’ Reviews Using the SVM Model with Enhanced Conjunction Rule-Based Approach

Abstract

Abstract: It is a tough task for tourism management to identify their user reviews and come up with solutions to the advancement of their tourism organizations. There are many social media reviews, and Tourism organizations face the challenge of evaluating numerous social media reviews to find solutions for advancing their organizations. It can be tough to physically assess all those reviews, social media has become a huge trend these days. People are constantly sharing their experiences and opinions on tourist places. By analyzing the sentiment of reviews, it may obtain useful information about the popularity of different tourist destinations. It's a great way to understand what people love about certain places. Sentiment classification is indeed a valuable tool in classifying reviews into different categories, aiding in decision-making. However, it's important to note that reviews often contain noisy content like typos and emoticons, which can affect the accuracy of the algorithms. Taking these aspects into consideration is crucial for achieving more accurate results. Decision-making and multiple class classification of reviews are possible with the application of sentiment classification. Sentiment analysis plays a crucial role in helping tourists make informed decisions about their travel destinations. In this particular paper, Using an Enhanced Conjunction Rule-Based Approach and Support Vector Machine (SVM) machine learning technique, the authors conducted sentiment analysis. They collected the dataset from different tourism review websites to train and evaluate their model. The findings of this paper are significant as they not only provide valuable insights in the field of tourism but also help in identifying the most appropriate algorithm for tourism-related analysis. The findings of this paper are significant as they not only provide valuable insights in the field of tourism but also help in identifying the most appropriate algorithm for tourism-related analysis. This information can greatly contribute to the development and improvement of tourism strategies and decision-making processes.