Python3Spiders/WeiboSuperSpider

微博爬虫及配套工具箱，微博用户、话题、评论采集一网打尽。图片下载、情感分析，地理位置、关系网络、spammer 机器人识别等功能应有尽有。Docs：https://buyixiao.github.io/blog/weibo-super-spider.html 配套可视化网站：https://buyixiao.github.io/blog/one-stop-weibo-visualization.html

What it solves

This project provides a comprehensive toolkit for collecting, analyzing, and visualizing data from Weibo. It simplifies the process of gathering information about users, topics, and comments, while offering built-in tools for tasks like image downloading, sentiment analysis, location tracking, and identifying automated bot accounts.

How it works

The tool is designed for ease of use and portability. It saves all collected data into CSV files that can be opened directly in Excel, meaning you do not need to set up or manage a database. Each feature is built as an independent script, allowing users to run specific functions without worrying about complex dependencies or system configurations.

Who it’s for

It is intended for learners and researchers interested in web scraping and data analysis. The project is designed to be user-friendly for those who want to quickly extract and visualize social media data without needing a heavy technical infrastructure.

Highlights

  • Collects comprehensive data including users, topics, comments, and social relationship networks.
  • Includes built-in analysis features for sentiment, geography, and bot (spammer) detection.
  • Stores data in simple CSV files compatible with Excel.
  • Features modular, independent scripts for each function.
  • Provides access to companion websites for online data visualization and network construction.

Related

  • Project
    tinyfish-io/bigset-oss

    An AI-powered tool that transforms plain English descriptions into verified, structured datasets from the live web with automatic refresh schedules.

  • Project
    semantalytics/awesome-semantic-web

    A curated, community-maintained directory of resources, standards, and tools for the Semantic Web and Linked Data ecosystem.

  • Project
    4paradigm/OpenMLDB

    An open-source machine learning database that provides a feature platform to ensure consistent, low-latency features for both ML training and online inference.

  • Project
    lotus-data/lotus

    LOTUS is a Python library for large‑scale, LLM‑driven data processing. It introduces *semantic operators* (LLM‑based map, filter, reduce, join, etc.) and an optimizer that batches and cascades model calls to improve accuracy while lowering API costs. The library supports both pure LLM operators and *agentic* operators that run tool‑using agents (e.g., a sandboxed Python REPL) on each data shard, enabling complex tasks like code‑base analysis, research synthesis, failure‑mode mining, document extraction, LLM‑as‑judge evaluation, and RAG. Install via `pip install lotus-ai`; usage revolves around creating a `Corpus`, specifying a task, and invoking `agent` or semantic operators. Backed by Stanford/UC Berkeley research and documented on ReadTheDocs, with community Discord and example notebooks.