Advanced Analytics (sentiment Analysis) with Qlik sense

Alanoud — Mon, 17 Apr 2023 12:43:22 GMT

Hello,

Actually, I'm trying to use Sentiment Analysis in Qlik sense.

I tried by some ways like:

PY-Tools by python, but it's not working, Also Advanced Sources (Amazon Comprehend) it's requires Qlik-Sense Cloud, but currently I need to use it with Qlik-Sense Desktop

So, if there is any API, Extension, Connections, Integration or another way can I use it.

I appreciate if you can attach example with Qlik-Sense Application and all necessary files as example.

Thank you,

Re: Advanced Analytics (sentiment Analysis) with Qlik sense

GTiscareno — Thu, 04 May 2023 21:32:32 GMT

you can try using other options like Some popular Sentiment Analysis tools include Google Cloud Natural Language API, Microsoft Azure Text Analytics, Amazon Comprehend, and IBM Watson.

You can Select GCC and create an API, for example below is small script using python

import pandas as pd from flask import Flask, jsonify from google.cloud import language_v1 # Authenticate with the Google Cloud Natural Language API client = language_v1.LanguageServiceClient.from_service_account_file('path/to/service_account.json') # Load data from Qlik Sense app qlik_data = pd.read_csv('path/to/data.csv') # Define function to perform Sentiment Analysis using the Google Cloud Natural Language API def analyze_sentiment(text): document = language_v1.Document(content=text, type_=language_v1.Document.Type.PLAIN_TEXT) sentiment = client.analyze_sentiment(request={'document': document}).document_sentiment return sentiment.score, sentiment.magnitude # Create a Flask app app = Flask(__name__) # Define a route for the Sentiment Analysis API @app.route('/api/sentiment_analysis') def sentiment_analysis(): results = [] for _, row in qlik_data.iterrows(): sentiment_score, sentiment_magnitude = analyze_sentiment(row['comment']) result = { 'id': row['id'], 'comment': row['comment'], 'sentiment_score': sentiment_score, 'sentiment_magnitude': sentiment_magnitude } results.append(result) return jsonify(results) # Run the Flask app if __name__ == '__main__': app.run(debug=True)

This will start the Flask development server, which you can access at http://localhost:5000. You can then make a GET request to http://localhost:5000/api/sentiment_analysis to retrieve the Sentiment Analysis results in JSON format.

topic Re: Advanced Analytics (sentiment Analysis) with Qlik sense in Integration, Extension & APIs

Advanced Analytics (sentiment Analysis) with Qlik sense

Re: Advanced Analytics (sentiment Analysis) with Qlik sense