Design and implementation of tourist attraction review analysis system based on python
Design and Implementation of a Python-based System for Analysis of Tourist Attraction Reviews
Complete download link: Design and implementation of tourist attraction review analysis system based on python
Article Directory
- Design and implementation of tourist attraction review analysis system based on python
- summary
- Chapter 1 Introduction
- 1.1 Research background
- 1.2 Research Purpose
- 1.3 Research content
- Chapter 2 Related technologies and tools
- 2.1 Python programming language
- 2.2 Data crawling and processing
- 2.3 Data analysis and mining
- Chapter 3 Acquisition and preprocessing of tourist attractions review data
- 3.1 Data source
- 3.2 Data cleaning and preprocessing
- Chapter 4: Comments on tourist attractions and emotional analysis
- 4.1 Sentiment analysis method
- 4.2 Construction of emotional dictionary
- Chapter 5: Theme extraction of tourist attractions comments
- 5.1 Theme extraction method
- 5.2 Theme model construction
- Chapter 6 System Design and Implementation
- 6.1 System architecture design
- 6.2 System function implementation
summary
Abstract "Design and Implementation of Tourism Attraction Review Analysis System Based on Python":
This paper proposes the design and implementation of a tourism attraction review analysis system based on Python. The system aims to help users better understand and select tourist attractions by analyzing their comments on the online travel platform.
The system is mainly divided intoData collection, data preprocessing, sentiment analysis and result display modules. First, the user's comment data on major travel websites is obtained through network crawling technology, and data cleaning and deduplication are carried out to ensure the quality and accuracy of the data. Then, use the PythonNatural Language ProcessingThe tool performs preprocessing operations such as word segmentation and stop words removal on the comment text for subsequent sentiment analysis. Next, a sentiment analysis algorithm is used to classify the emotional polarity of the comment text, and judge the positive, negative or neutral tendencies of the comment to understand the user's evaluation of the attractions. Finally, the analysis results are displayed in the form of a chart through data visualization technology, so that users can intuitively understand the evaluation indicators of each attraction.
During the implementation of the system, various libraries and tools in Python are used, such as BeautifulSoup, jieba, SnowNLP and matplotlib, which improves the efficiency and effectiveness of data processing and visualization. Moreover, the system design also takes into account the user's convenience and interactivity, and adopts a concise and clear user interface to facilitate users to operate.
Through the design and implementation of this system, people can effectively help understand and select tourist attractions. Users can obtain the comments and sentiment analysis results of various attractions through this system to provide reference for their travel. At the same time, the system can also provide tourism practitioners with a reference basis for evaluating the reputation of attractions, helping them optimize and improve. In short, the Python-based tourist attraction review analysis system is a research topic with practicality and application prospects.