A company has a 1,000 sentences with sentiments categorized as positive, neutral, or negative. Which modeling technique should a Machine Learing Specialist select for training a baseline sentiment…

K-means is an unsupervised clustering algorithm, whereas the problem is a supervised classification task with labeled sentiment categories.

Multinomial logistic regression is a simple, computationally efficient, and interpretable algorithm specifically designed for multi-class classification problems, making it an excellent choice for establishing a quick and effective baseline sentiment model on labeled textual data.

Recurrent Neural Networks (RNNs) are powerful deep learning models for sequence data, but they are generally more complex, computationally intensive, and require more data to train effectively than what is typically considered a baseline model, especially with only 1,000 sentences.

Transfer learning, while highly effective for natural language processing, involves fine-tuning large pre-trained models and is typically a more advanced approach rather than a simple baseline, incurring higher computational and resource overhead.