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.
