Basic Pega NLP exercise for Twitter

-

Here I am sharing a small, basic Natural Language Processing (NLP) exercise using Pega Infinity V84 to analyze the Twitter's tweet data and capture the sentiments into a Pega case. Before we start this exercise, we need to have twitter's API keys. For that, we need to login into the Twitter's developer site using twitter login credentials and create an app in it.
https://developer.twitter.com/

Here is the details for your info and below are the simple steps to be followed to work on this exercise J

1) We need to build a Data Flow: TwitterDataFlow to capture the Twitter configuration (TwitterDS) , Text analyzer process (TweetsAnalyzer) and a Pega case (TwitterCase) to be used to capture the data

2) Took the Pega NLP model: TwitterSentimentModels and used in the Text analyzer step and created a sample NL file (TwitterSentimentLexicon) in csv format to recognize the words/sentiments

3) Create a sample model(TwitterSentimentModels) by using the Predictive analytics process from Prediction Studio given by Pega

4) Finally, capture the tweet data into a Pega case (TwitterCase) after text analysis done

Here is the snapshot of all the used rules for this simple work and you can follow the arrows & text to understand the above steps clearly J



To test the above work, here is the sample tweets used from my Twitter acct and captured the sentiments è Positive, Negative and Neutral based on the Lexicon file used above. Of course, we can use different language texts for identifying the sentiments and we need to mention the corresponding Language code in the csv file while importing into that Lexicon rule. Used a simple tag => #pisupati and analyze the tweet’s text from the posted tweets and those tweets data is processed from Pega NLP process to create sample Twitter case to store in work item J




Though the whole exercise looks very simple after seeing  the above explanation but there is some hard work involved to bring this exercise in a simplified format J. Those who are not aware of what is NLP and where it is used in AI etc etc, can be checked from Pega community or google them.

References:
1) Pega Community Site
2) Pega Developer's Help
3) Google.com

Popular posts from this blog

Connecting Claude to Pega Infinity 25.1.3 via MCP — Step-by-Step

-
Image
Enable AI-powered access to your Pega Investigative Case Management data directly from Claude prompts using the Model Context Protocol (MCP) and OAuth 2.0. What is MCP and why connect Pega to Claude? The Model Context Protocol (MCP) is an open standard that lets AI assistants like Claude securely call tools and read data from external systems — all from a natural-language prompt. Think of it as a universal adapter between your enterprise systems and AI. Pega Infinity V25.1.3 ships with a first-class MCP service built on its existing REST framework. Once enabled, Claude can: Query and summarize investigative cases without switching apps Retrieve assignment details and case status on demand Trigger Pega workflows from conversational prompts Version requirement: This guide is based on Pega Infinity V25.1.3 (Community Edition) with the Rule-Service-MCP rule type. Earlier versions may not include native MCP support. Architecture overview The connection uses three layers: Claude as the AI c...
Read more

itextpdf API to generate PDF doc from an image file using Pega PE

-
Image
Probably some of the techies might have an idea on an opensource called " itextpdf " (earlier name is lowagie ) to convert an image file(png,jpeg,jpg,tiff,gif etc.) to a PDF file. It is very simple to use this API in Pega function to build the logic using java code. Moreover, this API comes in one single jar file and we need to import it in Pega before we start using it.  I see the conversion comes good in PDF file but not sure if any limitations of this API in terms of conversion is like formatting, font, images etc. So far so good and may be we can keep this API as one of our solutions while working on the requirements accordingly.  Heard that same API can be used to convert the text file to PDF but I didn't explore on it. Those who are interested to work on text -> PDF can take a look at the documentation. Don't hesitate to contact me offline for any further discussion in this context :) :) Prerequisites for doing this exercise: 1) itextpdf 5.4.0 jar file 2) Peg...
Read more

Understanding of Hugging Face platform for AI/ML platform

-
Image
Hugging Face is a platform that provides tools and libraries for natural language processing (NLP), machine learning, and artificial intelligence. It is known for its Transformers   library, which offers pre-trained models for tasks such as text classification, translation, question answering, and more. Hugging Face also hosts a model hub where users can share and discover pre-trained models. Hugging Face is useful in various domains, including: Natural language processing (NLP): Provides tools and pre-trained models for tasks like text classification, translation, sentiment analysis, and question answering. Machine learning research: Offers state-of-the-art models and datasets for researchers to experiment and innovate. Data science and analytics: Helps data scientists analyze and interpret large volumes of text data. AI application development: Enables developers to build AI-powered applications with ease using pre-trained models and APIs. A very simple exercise using the google...
Read more