Create a sentiment analysis of Linkedin comments leveraging Azure Cognitive Services

by | Sep 16, 2022

Social media platforms have become a vital tool for businesses to connect with their customers and stakeholders. LinkedIn, the professional networking site, provides a valuable platform for companies to engage with their target audience and gather valuable feedback. One way to extract insights from LinkedIn is to use sentiment analysis, which is the process of automatically analyzing the emotional tone of a piece of text. With the help of Azure Cognitive Services, it is possible to analyze the sentiment of comments on a LinkedIn account.

Here are the steps to use Cognitive Services for a sentiment analysis of a LinkedIn account:

  1. Set up an Azure Cognitive Services account To use Azure Cognitive Services, you need to set up an account on the Azure portal. Once you have set up the account, you can access various APIs, including the Text Analytics API, which can be used for sentiment analysis.
  1. Authenticate with LinkedIn To access the comments on a LinkedIn account, you need to authenticate with LinkedIn’s API. This can be done by creating an application in the LinkedIn developer portal and generating an access token.
  1. Fetch comments Once you have authenticated with LinkedIn, you can use the API to fetch the comments on a particular post. This can be done by making a call to the LinkedIn API and passing the post ID.
  1. Analyze sentiment After fetching the comments, you can use the Text Analytics API to analyze the sentiment of each comment. The API returns a score between 0 and 1, where 0 indicates negative sentiment and 1 indicates positive sentiment. You can use this score to classify each comment as positive, negative, or neutral.
  1. Visualize sentiment Finally, you can visualize the sentiment of the comments using a dashboard or visualization tool. This can be done by creating a chart that shows the percentage of comments that are positive, negative, or neutral.

By following these steps, you can extract valuable insights from comments on a LinkedIn account. Sentiment analysis can help you understand the emotional tone of your customers and stakeholders, and identify any issues or concerns they may have. This can help you improve your products and services, and enhance your overall customer experience.

In conclusion, by using Azure Cognitive Services for sentiment analysis of a LinkedIn account, businesses can gain valuable insights that can help them better understand their customers and stakeholders. This can lead to improved products and services, enhanced customer experiences, and ultimately, increased customer satisfaction and loyalty.

Here’s a sample code in Python using Azure Cognitive Services Text Analytics API for sentiment analysis of comments on a LinkedIn post:

import os

import requests

from azure.cognitiveservices.language.textanalytics import TextAnalyticsClient

from msrest.authentication import CognitiveServicesCredentials

# Define the LinkedIn API endpoints

access_token = ‘<YOUR_LINKEDIN_ACCESS_TOKEN>’

post_id = ‘<YOUR_LINKEDIN_POST_ID>’

comments_endpoint = f’https://api.linkedin.com/v2/socialActions/{post_id}/comments’

# Set up the Azure Text Analytics client

endpoint = ‘https://<YOUR_COGNITIVE_SERVICES_REGION>.api.cognitive.microsoft.com’

key = ‘<YOUR_COGNITIVE_SERVICES_KEY>’

credentials = CognitiveServicesCredentials(key)

client = TextAnalyticsClient(endpoint=endpoint, credentials=credentials)

# Fetch comments from LinkedIn

response = requests.get(comments_endpoint, headers={‘Authorization’: f’Bearer {access_token}’})

comments = [comment[‘text’] for comment in response.json()[‘elements’]]

# Analyze sentiment using Azure Text Analytics

sentiments = client.sentiment(documents=comments)

# Print the sentiment analysis results

for index, comment in enumerate(comments):

    print(f’Comment {index+1}: {comment}’)

    print(f’Sentiment: {sentiments[index].sentiment}\n’)

This code fetches the comments from a LinkedIn post using the LinkedIn API, analyzes the sentiment of each comment using Azure Cognitive Services Text Analytics API, and prints the sentiment analysis results for each comment. Note that you will need to replace <YOUR_LINKEDIN_ACCESS_TOKEN>, <YOUR_LINKEDIN_POST_ID>, <YOUR_COGNITIVE_SERVICES_REGION>, and <YOUR_COGNITIVE_SERVICES_KEY> with your own values.