Google Professional Machine Learning Engineer
Get started today
Ultimate access to all questions.
You are an ML engineer working in the contact center of a large enterprise, tasked with building a sentiment analysis tool to predict customer sentiment from recorded phone conversations. Your goal is to develop a model that accurately determines sentiment while ensuring that demographic factors such as gender, age, and cultural differences do not skew the results or affect any stage of the model development pipeline. To achieve a fair and unbiased sentiment analysis, which approach should you take?
You are an ML engineer working in the contact center of a large enterprise, tasked with building a sentiment analysis tool to predict customer sentiment from recorded phone conversations. Your goal is to develop a model that accurately determines sentiment while ensuring that demographic factors such as gender, age, and cultural differences do not skew the results or affect any stage of the model development pipeline. To achieve a fair and unbiased sentiment analysis, which approach should you take?
Explanation:
The correct answer is A: Convert the speech to text and extract sentiments based on the sentences. This approach allows for a comprehensive analysis of the sentiment expressed in entire sentences, which helps to take into account the context in which words are used. By focusing on sentences rather than individual words or voice recordings, this method minimizes the impact of variations in language use that may be influenced by gender, age, or cultural differences. Syntactic analysis could introduce biases from different sentence structures used in various cultures, and analyzing the words directly could miss important contextual information that affects sentiment. Converting speech to text and focusing on sentences provides a balanced and contextually aware approach to sentiment analysis.