How to engage in big data: hot topics and structured analysis on the entire network in the past 10 days
In today's era of information explosion, big data has become a core tool for corporate and personal decision-making. How to efficiently collect, process and analyze big data? This article combines the hot topics on the Internet in the past 10 days, displays hot content through structured data, and discusses the practical methods of big data.
1. Inventory of hot topics on the Internet in the past 10 days
The following are hot topics compiled based on social media, news platforms and search engines (data as of October 2023):
| Ranking | hot topics | Number of discussions (10,000) | Main platform |
|---|---|---|---|
| 1 | iPhone 15 release and user experience | 1200 | Weibo, Twitter, technology forums |
| 2 | OpenAI releases DALL-E 3 | 950 | Reddit, Zhihu, technology community |
| 3 | Global Climate Change Summit Progress | 780 | News sites, YouTube |
| 4 | "Oppenheimer" movie controversy | 650 | Douban, TikTok |
| 5 | Cryptocurrency market volatility | 520 | Financial media, Telegram |
2. How to use big data to analyze hot spots?
1.Data collection: Capture multi-platform data through crawler tools (such as Scrapy) or APIs (such as Twitter API) to ensure breadth of coverage and timeliness.
2.Data cleaning: Use Python (Pandas library) or ETL tools (such as Informatica) to process noisy data, such as deduplication and missing value filling.
| steps | Tools/Techniques | Example |
|---|---|---|
| Collect | Scrapy, BeautifulSoup | Capture hot search keywords on Weibo |
| Clean | Pandas, OpenRefine | Remove duplicate comments |
| analysis | SQL, TensorFlow | sentiment analysis |
3.data analysis: Mining trends through natural language processing (NLP) or machine learning models such as LSTM. For example, a sentiment analysis was performed on the "iPhone 15" topic and it was found that 35% of users' negative feedback on battery life accounted for 35%.
3. Challenges and Solutions of Big Data Applications
Challenge 1: Data silosThe data formats of different platforms are not uniform, and a standardized data warehouse (such as Hadoop HDFS) needs to be established.
Challenge 2: Real-time requirementsStream processing frameworks (such as Apache Kafka) can achieve second-level response and are suitable for public opinion monitoring.
4. Future Outlook
With the popularization of AI technology, big data analysis will become more intelligent. For example, combine GPT-4 to automatically generate hotspot reports, or mine topic correlations through graph database (Neo4j).
Through structured data and multi-dimensional analysis, "big data" is no longer a problem, but the core engine driving business growth.
check the details
check the details
en
