Public Opinion Monitoring on the Internet: A Real-Time Data Acquisition Approach

Web Technologies
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Public Opinion Monitoring on the Internet: A Real-Time Data Acquisition Approach

In today’s digital age, monitoring public opinion on the internet has become a crucial aspect of understanding the collective sentiment of the masses. This article presents a preliminary approach to achieving real-time access to internet data, leveraging search engine results to monitor public opinion and predict behavior. We will delve into the implementation code and discuss the results achieved through this methodology.

Public Opinion Monitoring: A Brief Overview

Public opinion monitoring involves analyzing online expressions and opinions to forecast behavior. Traditional methods often rely on search engines, which can be limiting in their scope and accuracy. However, by combining search engine results with advanced data extraction techniques, we can gain a more comprehensive understanding of public opinion.

Implementation Code: A Real-Time Data Acquisition Approach

To achieve real-time access to internet data, we employed the following implementation code:

import requests
from lxml import etree
import os
import sys

def get_data(wd):
    # Set user agent header to mimic a browser
    headers = {
        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
    }

    # Define target URL structure
    target_url = "https://www.baidu.com/s?wd=" + str(wd)

    # Send GET request to target URL
    data = requests.get(target_url, headers=headers)

    # Parse HTML content using Xpath
    data_etree = etree.HTML(data.content)

    # Extract data list
    content_list = data_etree.xpath('//div[@id="content_left"]/div[contains(@class, "result c-container")]')

    # Define string returned
    result = ""

    # Get title, content, and links
    for content in content_list:
        result_title = "<title>"
        bd_title = content.xpath('.//h3/a')
        for bd_t in bd_title:
            result_title += bd_t.xpath('string(.)')
        result_content = "<Content>"
        bd_content = content.xpath('.//div[@class="c-abstract"]')
        for bd_c in bd_content:
            result_content += bd_c.xpath('string(.)')
        result_link = "<link>" + str(list(content.xpath('.//div[@class="f13"]/a[@class="c-showurl"]/ @href'))[0])
        result_list = [result_title, "\n", result_content, "\n", result_link, "\n", "\n"]
        for result_l in result_list:
            result += str(result_l)

    return result

def save_data_to_file(file_name, data):
    # Create folder if it does not exist
    if os.path.exists("./data/"):
        pass
    else:
        os.makedirs("./data/")

    # Save data to file
    with open("./data/" + file_name + ".txt", "w+") as f:
        f.write(data)

def main():
    wd = ""
    try:
        wd = sys.argv[1]
    except:
        pass

    if len(wd) == 0:
        wd = "Naruto"

    str_data = get_data(wd)
    print(str_data)
    save_data_to_file(wd, str_data)

if __name__ == '__main__':
    main()

Results Achieved

Through this implementation code, we were able to achieve real-time access to internet data, leveraging search engine results to monitor public opinion and predict behavior. The preliminary results demonstrate the potential of this approach in understanding collective sentiment and forecasting behavior.

Conclusion

Public opinion monitoring is a crucial aspect of understanding the collective sentiment of the masses. By leveraging search engine results and advanced data extraction techniques, we can gain a more comprehensive understanding of public opinion. The implementation code presented in this article demonstrates a real-time data acquisition approach, which can be further refined and improved upon.