Sentiment analysis of tourist reviews examines common points of view to help users choose destinations, accommodations, and services. Provides businesses with information on customer satisfaction and helps travelers make better choices based on a variety of common experiences. The proposed method uses a mix of the Skip-Gram models and Continuous Bag of Words in addition to Word2Vec vectorization to transform textual data into numerical values. This method maintains the sentiment of the text and also captures the context and word relationships. Convolutional neural networks boost the accuracy of classification and feature extraction. Along with CNN, models like Random Forest, AdaBoost, LSTM, and CNN-BiLSTM have also been used in order to hold a comparison between them. Among these, the CNN model showed the highest accuracy of 96.3 %. This system provides users with specific recommendations for vacation spots, accommodations, and activities.