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

Question

BlackTom AI · Accepted Answer

To evaluate the question, we must compare how each option aligns with an unsupervised approach to sentiment analysis.
Option 1: Lexicons. Lexicon-based methods rely on predefined lists of words with sentiment scores and can operate without labeled training data, which fits the unsupervised paradigm for sentiment analysis. They use the strength of word-level sentiment cues to infer the overall sentiment of text.
Option 2: Topic modeling. Topic modeling (e.g., LDA) is a classic unsupervised technique to discover latent themes in documents, but it does not directly measure sentiment. While it can separate topics, it doesn’t inherently provide sentiment polarity or sentiment-aware scoring.
Option 3: Text classification. Text classification is typically a supervised learning task that uses labeled examples (positive/negative, etc.) to learn a mapping from text to categories. This requires labeled data, which is not characteristic of unsupervised methods.
Option 4: Online surveys. Online surveys are a data collection method rather than an analytical technique. They do not constitute a machine learning approach and do not address the unsupervised sentiment analysis paradigm.

In summary, option 1 is aligned with an unsupervised sentiment analysis approach because it relies on prebuilt sentiment knowledge without requiring labeled training data, whereas the other options either focus on different unsupervised techniques without direct sentiment scoring, or rely on labeled data, or describe data collection rather than analysis.

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

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

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