Developing a Model for Sentiment Analysis Technique in the field of Tourism using Deep Learning
Harsh Arora1, Mamta Bansal2
1Ms. Harsh Arora, Assistant Professor, (IT) Institute of Innovation, Technology and Management, Janakpuri, New Delhi, India.
2Dr. Mamta Bansal, Professor, Shobhit University, Meerut, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 30, 2020. | PP: 456-462 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7390038620/2020©BEIESP | DOI: 10.35940/ijrte.F7390.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper provides a platform for analyzing and summarizing the sentiments expressed by users or customers in the field of online tourism. The objective of this research is to analyze online reviews of all the users to propose a new optimized business model to improve present services of business organization to enhance profit and customer satisfaction. The proposed system filters tourism online reviews and classifies them using sentimental technique with the help of deep learning technique. Deep learning technique will not only identify the polarity of online reviews but also recognizes relevant patterns deeply to find the hidden reviews details. After applying the deep learning technique, the results will be generated through which we can find the inferences. These inferences would provide a great help for improvisation of the subject. In this research a new optimized business model will be implemented using deep learning technique so that we would be able to compare new business model with the present system [1]. The relevance of this research lies in helping tourism industries to understand the social sentiment of their brand, product or service while monitoring online conversations. It helps in enhancing business profits by running online websites throughout by giving best services to the online users or customers.
Keywords: Machine Learning, Deep Learning, Sentimental Analysis, Online Tourism.
Scope of the Article: Deep learning.