Author: Zen and the Art of Computer Programming
1 Introduction
This article mainly uses the method of crawling Baidu Tieba data to capture, analyze and display the posts and related information published by users in Tieba. The mentioned crawler includes requestsa library written in the Python language for data acquisition, analysis, storage, etc., based on BeautifulSoupthe library to analyze the structure of the webpage; at the same time, it also needs to use MongoDBa database for data storage. In addition, data cleaning, text processing, data visualization and other aspects will also be involved. Therefore, this article will elaborate on the working principle and key technical points of each step of the crawler, and give specific code examples.
2. Concept and Terminology Explanation
2.1 Data Definition
First, we need to understand the data structure of Baidu Tieba. Baidu Tieba is a modern community built on search engines. Users can express their views, complaints, comments or questions here, and can also respond to other people's suggestions. Its data structure is shown in the figure below:
Among them, nodes are various objects such as users and replies, and edges represent various relationships. For example, user A follows user B, which is a follow edge with directionality; user A replies to user C's post P, which is also a reply edge, and can form a descendant tree structure.
In addition, each node has a unique identifier id, and different types of nodes have different attributes. For example, user nodes have attributes such as user name, birthday, signature, and rating, and topic post nodes have attributes such as title, text, and creation time. Therefore, Tieba data consists of multiple types of nodes, forming a huge network.
2.2 Technical Features
Due to the huge amount of data, traditional methods based on database query or text analysis are inefficient and cannot directly handle such complex data. Therefore, this article adopts the method of Web Scraping, using the existing