Open AccessRecommendation System for Tourist Reviews using Aspect Based Sentiment Classification
Author(s) -
Kande Trupti,
Hritik Shah
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40865
Subject(s) - tourism , sentiment analysis , computer science , variety (cybernetics) , data science , artificial intelligence , machine learning , recommender system , moment (physics) , data mining , geography , physics , archaeology , classical mechanics
To improve services, the tourism industry makes use of a large amount of data collected from a variety of sources. Because of the easy availability of feedback, evaluations, and impressions from a wide range of visitors, tourism planning has become both rich and complex. As a result, the tourism industry faces a significant challenge in determining tourist preferences based on the data collected. Unfortunately, some user comments are meaningless and difficult to comprehend, making it difficult to make recommendations. Approaches to sentiment classification that are based on aspects have shown promise in terms of reducing noise. At the moment, there isn't a lot of work being done on aspect-based sentiment and classification. Aspect-based sentiment classification recommendation methods are introduced in this paper, which employ deep learning algorithms to not only classify aspects quickly, but also to perform classification tasks with high accuracy. A series of experiments on real-time review classification have been conducted to determine how effective the framework is at assisting tourists in locating the best location, hotel, and restaurant in a region. Keywords: Classification, Deep Learning, Tourist Reviews, Aspect Based Sentiment Analysis.