You work for a hotel and have a dataset that contains customers’ written comments scanned from paper-based customer feedback forms, which are stored as PDF files. Every form has the same layout. You need to quickly predict an overall satisfaction score from the customer comments on each form. How should you accomplish this task?
Using Document AI's custom extractor is beneficial because it can be tailored to accurately extract the customer comments from a specific layout present in the PDF forms. The Natural Language API's analyzeSentiment feature is designed to analyze the overall sentiment of a text, which directly correlates with predicting an overall satisfaction score. This combination ensures both precise extraction of relevant text and effective sentiment analysis.
Document AI custom extractor: Since the layout of the feedback forms is consistent, training a custom extractor in Document AI allows for efficient and accurate extraction of the specific comments section. This ensures the Natural Language API receives the relevant text for sentiment analysis. Natural Language API - analyzeSentiment: This functionality within the Natural Language API is specifically designed to analyze sentiment in a piece of text. It provides an overall sentiment score that can be mapped to a satisfaction score (e.g., high positive sentiment translates to high satisfaction).
A. Vision API - parseText: While the Vision API can extract text from PDFs, it wouldn't necessarily target the specific comments section without a custom parser. B. Natural Language API - analyzeEntitySentiment: This feature focuses on sentiment analysis for named entities within the text. It might not be ideal for overall satisfaction extraction from general customer comments.
I go with A, because "you need quickly predict", no time for fine-tunning.
Precision in text extraction: Document AI is specifically designed for extracting text from structured documents like forms, ensuring accurate extraction of comments, even with varying handwriting styles. Custom model for form layout: Training a custom extractor tailored to the hotel's feedback form layout further enhances accuracy and targets the relevant comments section effectively. Sentiment analysis: Natural Language API's analyzeSentiment feature analyzes overall sentiment in a text block, aligning with the goal of deriving overall satisfaction scores.
Document AI custom extractor: This allows you to tailor the text extraction specifically to the layout and format of your customer feedback forms, ensuring accurate capture of the comments section. Natural Language API analyzeSentiment: This feature analyzes the extracted text and provides an overall sentiment score, which can be used to gauge customer satisfaction.
My answer: Letter C Document AI is a suitable tool for cases where there are patterns of forms or documentation. Additionally, it is possible to directly read PDF files. In the Natural Language API, the analyzeSentiment function can determine the overall sentiment, as the text asks, "You need to quickly predict an overall satisfaction." The analyzeEntitySentiment function provides a score for each entity or word found. https://cloud.google.com/natural-language/docs/basics
quickly predict an overall satisfaction -> a
no sorrt, it's C, you need doc AI
C & D are overkill We don't care about entities sentiment -> B is out Left with A and https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/analyzeSentiment
C is right Document AI custom extractor: Allows you to train a custom model to extract relevant information (in this case, customer comments) from the PDF files. Natural Language API analyzeSentiment feature: Analyzes the sentiment of the extracted text to predict an overall satisfaction score.