Hotel-Review-Sentiment-analysis | based travel scheduling and booking
kandi X-RAY | Hotel-Review-Sentiment-analysis Summary
kandi X-RAY | Hotel-Review-Sentiment-analysis Summary
Hotel-Review-Sentiment-analysis is a JavaScript library. Hotel-Review-Sentiment-analysis has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.
Web-based travel scheduling and booking has become one of the major commercial uses.Hotel booking websites use online rating and customer input to support the decision-making process of the client, but reviews provide a better insight into the hotel, but most travelers do not have time or patience to read all reviews. This research analyzes the ratings of hotels and provides information that may miss. The comments and metadata are crawled from the website and grouped according to some specific aspects into pre-defined categories.Here, we try to make efficient reviews sentiment analysis on “booking.com” hotel reviews and apply NLP to pre-processing of data. After that identifying the subjective information in text and classifying each piece of data as positive, negative and neutral response. This pre-processing data convert into vector and apply convolution neural network(CNN) algorithm on vector matrix and outcomes of CNN represent as pie-chart and bar-chart using Django library. This chart are define base on categories like room, food, cleanliness, service, staff, nature view, facilities.
Web-based travel scheduling and booking has become one of the major commercial uses.Hotel booking websites use online rating and customer input to support the decision-making process of the client, but reviews provide a better insight into the hotel, but most travelers do not have time or patience to read all reviews. This research analyzes the ratings of hotels and provides information that may miss. The comments and metadata are crawled from the website and grouped according to some specific aspects into pre-defined categories.Here, we try to make efficient reviews sentiment analysis on “booking.com” hotel reviews and apply NLP to pre-processing of data. After that identifying the subjective information in text and classifying each piece of data as positive, negative and neutral response. This pre-processing data convert into vector and apply convolution neural network(CNN) algorithm on vector matrix and outcomes of CNN represent as pie-chart and bar-chart using Django library. This chart are define base on categories like room, food, cleanliness, service, staff, nature view, facilities.
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Hotel-Review-Sentiment-analysis has a low active ecosystem.
It has 1 star(s) with 1 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
Hotel-Review-Sentiment-analysis has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Hotel-Review-Sentiment-analysis is current.
Quality
Hotel-Review-Sentiment-analysis has no bugs reported.
Security
Hotel-Review-Sentiment-analysis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
Hotel-Review-Sentiment-analysis does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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Hotel-Review-Sentiment-analysis releases are not available. You will need to build from source code and install.
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Hotel-Review-Sentiment-analysis Key Features
No Key Features are available at this moment for Hotel-Review-Sentiment-analysis.
Hotel-Review-Sentiment-analysis Examples and Code Snippets
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Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install Hotel-Review-Sentiment-analysis
You can download it from GitHub.
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