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Pegasystems PEGACPDS88V1 Exam - Topic 7 Question 45 Discussion

Actual exam question for Pegasystems's PEGACPDS88V1 exam
Question #: 45
Topic #: 7
[All PEGACPDS88V1 Questions]

A telecom company is interested in improving customer engagement on social medi

a. However, there are hundreds of relevant messages posted every day, and it is not practical for customer service representatives (CSRs) to review and respond to all messages. Instead, CSRs should focus on negative messages. What do you need to analyze the incoming messages?

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Suggested Answer: C

A text categorization model is a type of text analytics model that can analyze the incoming messages and assign them to predefined categories, such as positive, negative, or neutral sentiment. This way, CSRs can focus on negative messages that require immediate attention or escalation. Reference: https://academy.pega.com/module/text-analytics/topic/creating-text-categorization-model


by Lashaunda at Jan 22, 2026, 05:09 PM

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Tamar
1 day ago
Forget the tech, just hire a bunch of interns to read through everything. Cheap labor is the solution!
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Luis
6 days ago
Machine learning algorithms are the key here. Train 'em on a dataset of messages and let the AI do the work.
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Olga
11 days ago
Definitely need some natural language processing to sort through all those messages. Can't have the CSRs going through them one by one.
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Lashon
17 days ago
Sentiment analysis would be the way to go. Gotta focus on those negative messages!
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Lisandra
22 days ago
I practiced a similar question about prioritizing customer feedback. It seems like categorizing messages could help CSRs focus on the most urgent issues.
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Eliz
27 days ago
We might also need to analyze the volume of messages over time to see if there are specific trends or spikes in negative feedback.
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Joana
2 months ago
I'm not entirely sure, but I remember something about using keywords to identify issues. Maybe we could create a list of common complaints?
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Lili
2 months ago
I think we need to implement some sort of sentiment analysis tool to filter out the negative messages from the positive ones.
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Edmond
2 months ago
This is a great opportunity to apply some of the concepts we've learned. I'm feeling pretty confident I can come up with a solid approach to tackle this problem.
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Aileen
2 months ago
Sounds like we need a robust text analytics solution to handle this. I'll need to research the latest techniques in natural language processing and machine learning.
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William
2 months ago
Sentiment analysis, got it. But what other data points do we need to consider? Probably things like message volume, keywords, user influence, etc.
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Billy
3 months ago
Okay, so we need to focus on the negative messages. I guess we'll have to find a way to automatically detect the sentiment of each message.
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Jutta
3 months ago
Hmm, this seems like a tricky one. I'll need to think carefully about how to analyze all those messages efficiently.
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