Scrapy implements news website data collection and analysis

With the continuous development of Internet technology, news websites have become the main way for people to obtain current affairs information. How to quickly and efficiently collect and analyze data from news websites has become one of the important research directions in the current Internet field. This article will introduce how to use the Scrapy framework to implement data collection and analysis on news websites.

1. Introduction to Scrapy framework

Scrapy is an open source web crawler framework written in Python, which can be used to extract structured data from websites. The Scrapy framework is based on the Twisted framework and can crawl large amounts of data quickly and efficiently. Scrapy has the following features:

1. Powerful functions - Scrapy provides many useful functions, such as custom requests and handlers, automatic mechanisms, debugging tools, etc.
2. Flexible configuration - The Scrapy framework provides a large number of configuration options that can be flexibly configured according to specific crawler needs.
3. Easy to expand - Scrapy's architectural design is very clear and can be easily expanded and secondary developed.

2. News website data collection

For the data collection of news websites, we can use the Scrapy framework to crawl news websites. The following takes Sina News website as an example to introduce the use of Scrapy framework.

1. Create a new Scrapy project

Enter the following command on the command line to create a new Scrapy project:

scrapy startproject sina_news

This command will create a new Scrapy project named sina_news in the current directory.

2. Writing Spider

In the new Scrapy project, you can implement web crawling by writing Spider. In Scrapy, Spider is a special Python class used to define how to crawl website data. The following is an example of a Spider for a Sina news website:

import scrapy

class SinaNewsSpider(scrapy.Spider):
    name = 'sina_news'
    start_urls = [
        'https://news.sina.com.cn/', # 新浪新闻首页
    ]

    def parse(self, response):
        for news in response.css('div.news-item'):
            yield {
                'title': news.css('a::text').extract_first(),
                'link': news.css('a::attr(href)').extract_first(),
                'datetime': news.css('span::text').extract_first(),
            }

Spider defines the rules for crawling news websites and the way to parse the response. In the above code, we define a Spider named "sina_news" and specify the starting URL as the Sina News homepage. At the same time, we also defined a parse function to parse the website's response.

In this parse function, we use CSS Selector syntax to extract the title, link and release time of the news, and return this information in the form of a dictionary.

3. Run the Spider

After completing the writing of the Spider, we can run the Spider and crawl the data. Enter the following command on the command line:

scrapy crawl sina_news -o sina_news.json

This command will start the "sina_news" Spider and save the crawled data to a file named sina_news .json JSON file.

3. News website data analysis

After completing the data collection, we need to analyze the collected data and extract valuable information from it.

1. Data Cleaning

When collecting data on a large scale, we often encounter some noisy data. Therefore, before conducting data analysis, we need to clean the collected data. The following uses the Python Pandas library as an example to introduce how to perform data cleaning.

Read the collected Sina news data:

import pandas as pd

df = pd.read_json('sina_news.json')

Now We got a data set of type DataFrame. Assuming that there is some duplicate data in this data set, we can use the Pandas library for data cleaning:

df.drop_duplicates(inplace=True)

The above line of code will delete the duplicate data in the data set .

2. Data Analysis

After data cleaning, we can further analyze the collected data. Here are some commonly used data analysis techniques.

(1) Keyword analysis

We can understand current hot topics by conducting keyword analysis on news titles. The following is an example of keyword analysis on Sina news titles:

from jieba.analyse import extract_tags

keywords = extract_tags(df['title'].to_string(), topK=20 , withWeight=False, allowPOS=('ns', 'n'))
print(keywords)

The above code uses the extract_tags function of the jieba library to extract the top 20 news titles keywords.

(2) Time series analysis

We can understand the trend of news events by counting news titles in chronological order. The following is an example of time series analysis of Sina news by month:

df['datetime'] = pd.to_datetime(df['datetime'])
df = df.set_index('datetime ')
df_month = df.resample('M').count()
print(df_month)

The above code converts the news release time to Pandas' Datetime type and Set to the index of the dataset. We then used the resample function to resample the months and calculate the number of news releases per month.

(3) Classification based on sentiment analysis

We can classify news by performing sentiment analysis on news titles. The following is an example of sentiment analysis on Sina news:

from snownlp import SnowNLP

df['sentiment'] = df['title'].apply(lambda x: SnowNLP(x ).sentiments)
positive_news = df[df['sentiment'] > 0.6]
negative_news = df[df['sentiment'] print('Positive News Count:' , len(positive_news))
print('Negative News Count:', len(negative_news))

The above code uses the SnowNLP library for sentiment analysis, and defines news with a sentiment value greater than 0.6 as positive news, and news with a sentiment value less than or equal to 0.4 as negative news.

4. Summary

This article introduces how to use the Scrapy framework to collect news website data and the Pandas library for data cleaning and analysis. The Scrapy framework provides powerful web crawler functions that can crawl large amounts of data quickly and efficiently. The Pandas library provides many data processing and statistical analysis functions that can help us extract valuable information from the collected data. By using these tools, we can better understand current hot topics and obtain useful information from them.

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