Which tool should be used to improve the validation accuracy?

A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset

Which tool should be used to improve the validation accuracy?
A . Amazon Comprehend syntax analysts and entity detection
B . Amazon SageMaker BlazingText allow mode
C . Natural Language Toolkit (NLTK) stemming and stop word removal
D . Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

View Answer

Answer: D

Explanation:

Term frequency-inverse document frequency (TF-IDF) is a technique that assigns a weight to each word in a document based on how important it is to the meaning of the document. The term frequency (TF) measures how often a word appears in a document, while the inverse document frequency (IDF) measures how rare a word is across a collection of documents. The TF-IDF weight is the product of the TF and IDF values, and it is high for words that are frequent in a specific document but rare in the overall corpus. TF-IDF can help improve the validation accuracy of a sentiment analysis model by reducing the impact of common words that have little or no sentiment value, such as “the”, “a”, “and”, etc. Scikit-learn is a popular Python library for machine learning that provides a TF-IDF vectorizer class that can transform a collection of text documents into a matrix of TF-IDF features. By using this tool, the Data Scientist can create a more informative and discriminative feature representation for the sentiment analysis task.

Reference: TfidfVectorizer – scikit-learn

Text feature extraction – scikit-learn

TF-IDF for Beginners | by Jana Schmidt | Towards Data Science

Sentiment Analysis: Concept, Analysis and Applications | by Susan Li | Towards Data Science