Exam Professional Machine Learning Engineer topic 1 question 112 discussion

Syntactic Analysis is not for sentiment analysis

A. Convert speech to text and extract sentiments based on sentences: This method focuses on the content of the conversation, minimizing the influence of factors like voice tone (which can be culturally or gender-specific). Sentiment analysis techniques can analyze the meaning and context of sentences to identify positive, negative, or neutral sentiment.

goes a step further by analyzing the structure and grammatical relationships within the sentences. This approach abstracts away from just the words used and focuses on how the words are put together, capturing deeper semantic and contextual information, less likely to be influenced by demographic variations in language use

The correct answer should be A. Word embeddings have static embeddings for the same words, while contextual embeddings vary depending on the context. "May’s sentence embedding adaptation of WEAT, known as the Sentence Embedding Association Test (SEAT), shows less clear racial and gender bias in language models and embeddings than the corresponding word embedding formulation" From: https://medium.com/institute-for-applied-computational-science/bias-in-nlp-embeddings-b1dabb8bbe20

This approach involves converting the speech to text, which allows you to analyze the content of the conversations without directly dealing with the speakers' gender, age, or cultural differences. By building a model based on the words, you can focus on the language used in the conversations to predict sentiment, making the model more inclusive and less sensitive to demographic factors. Option A could be influenced by the syntactical nuances and structures used in different cultures, and option C might be impacted by the variations in voice tones across genders and ages. Option B, on the other hand, relies on the text content, which provides a more neutral and content-focused basis for sentiment analysis.

B: People of different cultures will often use difference sentence structures, so words would be safer than sentences

building a model based on words, may also be effective but could potentially be influenced by factors such as accents, dialects, or language variations that may differ between speakers.extracting sentiment directly from voice recordings, may be less accurate due to the subjective nature of interpreting emotions from audio alone.using syntactical analysis, may be useful in certain contexts but may not capture the full range of sentiment expressed in a conversation. Therefore, A provides the most comprehensive and unbiased approach to sentiment analysis in this scenario.

Answer A

By working directly with the audio data, you can account for important aspects like tone, pitch, and rhythm of speech, which might provide valuable information regarding sentiment.

vote for A between words and sentences: Age and gender considerations: Sentences provide a broader view of sentiment that can help mitigate age and gender biases. Analyzing at the sentence level allows you to observe sentiment patterns across various demographic groups, which can help identify any biases that may arise. By considering the overall sentiment expressed in sentences, you can minimize the impact of individual words that might carry specific biases.

There is the possibility for a more sophisticated architecture for an audio processing pipeline, and the “not impact any stage of the model development pipeline and results” somewhat calls for a more holistic answer: https://cloud.google.com/architecture/categorizing-audio-files-using-ml#converting_speech_to_text. Plus, it adds “voice emotion information, related to an audio recording, indicating that a vocal utterance of a speaker is spoken with negative or positive emotion”: https://patents.google.com/patent/US20140220526A1/en.

Can anyone explain how to choose between words and sentences? I feel like the model could pick up bias from both

I agree with qaz09. To avoid demographical variables influence model shoud be built on the words.

Answer A

For "ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results" I think the model should be built on the words rather than sentences

A makes sense, to me.

A Convert the speech to text and extract sentiments based on the sentences.