Tourist Place Reviews Sentiment Classification Using Machine Learning Techniques
Author(s):
Satya Mohan Chowdary G, K. PAVANASUPRIYA, V. DEVA MANOJNA, A. MADHAVA SAI, B. GANESH
Keywords:
Abstract
Social media is growing trend now a days. Every day millions of user review and rate tourist places on tourism websites. Sentiment analysis can be performed over these reviews which will be helpful to find tourist place popularity. Based on sentiment analysis result, tourist can easily decide tour destination to be visited. In this paper sentiment analysis has been implemented using machine learning approach. The Dataset has been collected from various tourism review websites. Here we have performed comparative study of feature extraction algorithms i.e. Count Vectorization, TFIDF Vectorization. Along with classification algorithms Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF). Performance of algorithms has been compared using various parameters like accuracy, recall, precision and f1-score. From experiment we found that TFIDF Vectorization feature extraction algorithm has improved accuracy of classification algorithm as compare to Count Vectorization for given review dataset. In sentiment classification of tourist place reviews TFIDF Vectorization+RF has given highest accuracy 86% for a research dataset used.
Article Details
Unique Paper ID: 152723

Publication Volume & Issue: Volume 8, Issue 4

Page(s): 193 - 197
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