In today's era of popular social media, understanding users' comments on Facebook is of great significance for brand management, market research and social media monitoring. Using Python, you can easily track and collect comments on Facebook for data analysis, sentiment analysis, or other related applications.

This article will guide you on how to use Python to track Facebook comments, helping you understand users’ opinions and feedback on specific topics, brands, or campaigns.

1. Preparation work

Before you begin, you need to make sure you have a Python environment installed and understand basic programming concepts. In addition, you need to make the following preparations:

Create a Facebook developer account: To access Facebook data, you need to create a Facebook developer account and create an application. Follow the instructions in Facebook's developer documentation to complete these steps.

Install necessary Python libraries: You need to install some Python libraries to handle network requests, parse HTML, and perform data analysis. These libraries include requests, BeautifulSoup, and pandas. You can install these libraries via pip using the following command:

shell

pip install requests beautifulsoup4 pandas

Get Facebook comment data: To get Facebook comment data, you need to use Facebook Graph API. In the Facebook Developer Center, find your app and get your API key and access token. These keys will be used for authentication and data access.

2. Write Python scripts

Next, we will write a Python script to get Facebook comment data. Please open your text editor, create a new file, and paste the following code into the file:

python

import requests

import json

import pandas as pd

from bs4 import BeautifulSoup

# Facebook API Key and Access Token

api_key = 'Your Facebook API key'

access_token = 'Your Facebook access token'

# Define the function to get Facebook comments

def get_facebook_comments(post_id):

     # Build request URL

     url = f"https://graph.facebook.com/{post_id}/comments?fields=id,message,created_time,like_count&access_token={access_token}"

     #Send HTTP request to obtain JSON data

     response = requests.get(url)

     response.raise_for_status() # Check whether the request is successful

     # Parse JSON data into a Python dictionary list

     comments = response.json()['data']

     # Process the comment data and return the results in pandas DataFrame format

     comments_df = pd.DataFrame(comments)

     comments_df.columns = ['id', 'message', 'created_time', 'like_count']

     return comments_df

# Call the function to get the comment data and print the results

post_id = 'ID of the target Facebook post' # Please replace with the actual post ID

comments_df = get_facebook_comments(post_id)

print(comments_df)

Note that you need to replace api_key and access_token with the actual values you obtained in Facebook Developer Center. Also, you need to replace post_id with the ID of the specific Facebook post you want to track. This ID can be looked up in the post URL and is usually a long numeric string.

3. Run scripts and test tracking functions

Save and run the script. If everything is fine, you will see the comment data obtained from Facebook printed on the console in DataFrame format. You can further process and analyze the data as needed, such as filtering comments within a specific time range, performing sentiment analysis on comments, or visualizing the data, etc.