• Jitendra Soni

The objective of this research paper is to compare the effectiveness of different natural language processing techniques in classifying Indian tourist place reviews as positive, negative, or neutral. The techniques studied are LSTM based classifier, and Naive Bayes classifier, and the dataset used comprises textual reviews obtained from various online platforms. To determine which technique performs better, we evaluated the performance of these classifiers using a range of metrics, including precision, recall, F1 score, and accuracy. Our findings reveal that outperforming both the LSTM and Naive Bayes classifiers. This study provides valuable insights into the effectiveness of different natural language processing techniques in sentiment analysis and can assist businesses in making informed decisions based on customer feedback.

Sentiment Analysis, NLP, LSTM, Deep Learning, Naïve bayes, Machine learning

"SENTIMENT ANALYSIS ON INDIAN TOURIST PLACES REVIEWS USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.145-148, April-2019, Available :http://www.jetir.org/papers/JETIR1904W20.pdf

"SENTIMENT ANALYSIS ON INDIAN TOURIST PLACES REVIEWS USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp145-148, April-2019, Available at : http://www.jetir.org/papers/JETIR1904W20.pdf