Which tool should be used to improve the validation accuracy?
Amazon Comprehend syntax analysis and entity detection
Amazon SageMaker BlazingText cbow mode
Natural Language Toolkit (NLTK) stemming and stop word removal
Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer
Explanations:
Amazon Comprehend syntax analysis and entity detection focus on extracting structure and entities from text, but it doesn’t specifically address vocabulary size or word frequency issues.
Amazon SageMaker BlazingText cbow mode is a powerful algorithm for text classification, but it does not directly handle vocabulary richness or word frequency issues. It focuses on fast training of word embeddings.
NLTK stemming and stop word removal can reduce vocabulary size and remove frequent but unimportant words. However, it might not be sufficient to address the specific issue of low word frequency leading to poor validation accuracy.
Scikit-learn’s TF-IDF vectorizer addresses issues of vocabulary richness and word frequency by adjusting the weight of words based on their inverse document frequency, helping improve model performance for sentiment analysis.
From my point of view, the answer is:
Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer
To the best of my knowledge, the answer is:
Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer