Answer: Semi-supervised learning

The correct answer is D) Semi-supervised learning. **Explanation:** Semi-supervised learning is the appropriate approach for this scenario because: 1. The company has "massive amounts of unlabeled posts" - this is the key characteristic 2. They want to learn contextual word relationships from this unlabeled data 3. They plan to fine-tune the model later for sentiment analysis (which would require some labeled data) **Why other options are incorrect:** - **A) Transfer learning**: This involves using a pre-trained model on one task and adapting it to another related task. While related, it doesn't specifically address the use of massive unlabeled data. - **B) Reinforcement learning**: This involves learning through trial-and-error interactions with an environment to maximize rewards, which is not applicable to learning from static text data. - **C) Self-supervised learning**: This is actually a closer alternative, but self-supervised learning typically creates its own labels from the data (like predicting masked words). However, the scenario specifically mentions "unlabeled posts" and the need to learn contextual relationships before fine-tuning, which aligns more with semi-supervised learning where a small amount of labeled data is combined with large amounts of unlabeled data. **Key distinction**: Semi-supervised learning leverages both labeled and unlabeled data, which matches the company's plan to use unlabeled posts first, then fine-tune (with labeled data) for sentiment analysis.

Author: Jin H