The supervised machine learning approach to sentiment analysis is based on:

Question

BlackTom AI · Accepted Answer

The question asks about the supervised machine learning approach to sentiment analysis and what it is based on.
Option 1: 'Online surveys' — This option describes a data collection method rather than a modeling approach. Sentiment analysis using supervised learning relies on labeled data, not on conducting online surveys as the core basis.
Option 2: 'Text classification' — This is the correct framing. In supervised learning, the task is to map text inputs to predefined labels (e.g., positive, negative, neutral), which is exactly what sentiment analysis models do when trained on labeled examples.
Option 3: 'Topic modeling' — Topic modeling (e.g., LDA) is typically an unsupervised technique used to discover latent topics in a corpus, not the supervised learning approach used for sentiment classification.
Option 4: 'Lexicons' — Lexicon-based methods use predefined word lists to infer sentiment and do not involve supervised learning from labeled data. While lexicons can be used for sentiment analysis, they do not constitute the supervised ML approach itself.
In summary, supervised sentiment analysis relies on labeled examples to perform text classification, distinguishing it from data collection methods, unsupervised topic discovery, or lexicon-based approaches.

SP2025.B69.DAT.562.24 COMPLETE Quiz #2 AFTER VIDEO 1 - Sentiment Analysis Methods

The supervised machine learning approach to sentiment analysis is based on:

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