What it Means to be Human: Emotions & AI

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Questions and Answers

Explain the difference between categorical and dimensional representations of emotions.

In a categorical representation, emotions are viewed as distinct categories (e.g., happiness, sadness). In a dimensional representation, emotions are plotted on a spectrum based on dimensions like valence, arousal, and dominance.

According to Paul Ekman's research, how many universal facial expressions of emotions are there?

Paul Ekman's research identifies seven universal facial expressions of emotions.

Describe the VAD model and what each dimension represents.

The VAD model has three dimensions: Valence (how positive or negative the emotion is), Arousal (the intensity of the emotion), and Dominance (the level of control).

In the context of sentiment analysis, how can the Euclidean distance be used?

Euclidean distance can be used to measure the similarity between emotions represented as vectors in a multi-dimensional space. Smaller distances indicate more similar emotions. Signup and view all the answers

What is sentiment analysis, and how is it typically used?

Sentiment analysis is an automated process of analyzing text data to classify it as negative, positive, or neutral. It's often used to gauge public opinion or customer feedback. Signup and view all the answers

Explain the difference between dictionary-based and machine learning-based sentiment analysis.

Dictionary-based sentiment analysis uses a list of words associated with different sentiments, while machine learning-based analysis trains a model on labeled text data. Signup and view all the answers

How is the overall sentiment score calculated in dictionary-based sentiment analysis?

The overall sentiment score is calculated as (number of positive words - number of negative words) / total number of words. Signup and view all the answers

What is the crowdsourcing and how it is related to sentiment analysis?

Crowdsourcing uses data collected from a large group of people. For sentiment analysis this could be used to curate the words and polarity scores used in a dictionary-based approach. Signup and view all the answers

What is VADER, and what are some heuristics it considers?

VADER is a dictionary-based sentiment analysis tool specially suited for social media. It considers heuristics like punctuation, capitalization, degree modifiers, and conjunctions. Signup and view all the answers

In the context of sentiment analysis, how can a dictionary-based approach be adopted using categorical emotion representation?

A dictionary can assign a polarity score to words which are associated with different basic emotions (e.g. anger, anticipation, disgust, fear, joy, sadness, surprise, and trust). Signup and view all the answers

What do you need to do, to use machine-learning for sentiment analysis?

You need examples of text with label to train the machine learning model. Signup and view all the answers

In what ways do "Bag of Words (BoW)" affect machine learning?

The machine learning approach may require more memory and computational resources, which is often very challenging, given requirements for traditional algorithms. Signup and view all the answers

Describe the limitations of using the Bag of Words (BoW) model in feature extraction for sentiment analysis.

BoW is inefficient in large vocabularies and is unable to share information. The semantic relationships between similar words are also ignored. Signup and view all the answers

Summarize the key goals of using word embeddings in feature extraction.

Word embeddings aim to reduce the dimensions required to represent a collection of vocabulary while embedding words in a different space (semantic relationships). Signup and view all the answers

What information is being represented, for the following: [King] – [man] + [woman] = ?

This is attempting to capture relationships between a man and woman across a heirarchy of power between King and Queen. After removing maleness from King, adding femaleness to King results in a transformation towards a Queen. Signup and view all the answers

Who was John Rupert Firth, and what was his contribution to sentiment analysis?

Firth was an English linguist working on language patterns in the 1950s, and stated "You shall know a word by the company it keeps" – Firth, J.R. (1957) Signup and view all the answers

What is the role of cosine similarity in the context of word embeddings, and how is it interpreted?

Cosine similarity's role is to measure of similarity between two vectors in a multi-dimensional space. If angle = 0°, cosine similarity = 1 (identical vectors) but If angle = 90°, cosine similarity = 0 (completely different vectors). Signup and view all the answers

Define the role of baseline regression as related to machine learning.

Regression is a supervised machine learning algorithm that gives a classification (output) to a new input after the model is trained. During training, labeled inputs are used. Signup and view all the answers

In the equation, y = f(w•x) + b What do each of the terms describe?

y is the result, f is the function being used, w is the trained weights, x is the input, and b is a bias. Signup and view all the answers

Explain how a sigmoid function is used to compute sentiment.

After passing terms through a sigmoid function, the result is mapped to the range [0,1]. Sentiment will then be classified depending on the threshold. If y>0.5 then sentiment = positive; otherwise, sentiment = negative. Signup and view all the answers

Describe two possible types of data that an input feature vector can represent when training a regression model for sentiment analysis.

Word embeddings can be used with vector representation which is a function of weights. Crafted rules could also be useful such as including an accounting for number/ polarity of certain phrases. Signup and view all the answers

What is the relationship between 'Happy' and 'Unhappy'. Further, where are each of their roots from, if any?

The prefix -un shifts 'Happy' to 'Unhappy'. More genearlly this describes how morphology shapes the form/meaning of words through the use of suffixes and prefixes, among other approaches. Signup and view all the answers

What is a language model (LM), and what are they used for in NLP?

Language models, or LMs, are statistical tools used to predict the next word(s) in a sequence. So, they are often used to predict patterns. Signup and view all the answers

What are some applications of sentiment analysis and how can businesses benefit from it?

Sentiment analysis can be used to monitor brand reputation, understand customer opinions, and track trends. Businesses can use this data to make informed decisions about product development, marketing, and customer service. Signup and view all the answers

How do sentiment analysis tools handle sarcasm and other forms of figurative language, and why can this be a challenge?

Sarcasm and irony can be a challenge for sentiment analysis tools because the literal meaning of the words may conflict with the intended sentiment. Advanced techniques such as contextual analysis and machine learning can help improve accuracy, but these remain challenging. Signup and view all the answers

What are some ethical considerations when using sentiment analysis, particularly in areas like political polling or customer feedback?

Sentiment analysis can inadvertently amplify biases present in the training data, leading to skewed predictions. In political polling, this could affect electoral processes. So, in customer feedback, it could lead to unfair product evaluations. Signup and view all the answers

What are some common preprocessing steps used on text data to improve machine learning model performance?

Data with text is improved for performance through: stop word removal, stemming, tokenization, and handling missing values. These steps are critical given the algorithm will use this to calculate key vectors. Signup and view all the answers

How does self-supervised learning work?

In self supervised learning an input can include some labels, where the missing labels need to be replaced. Then, the missing labels can be inferenced. These have also been used toward training machine learning models. Signup and view all the answers

Describe a time that required communications with more than one person. Then, label the context (i.e. bump into a friend at a bus stop, etc), the content, the environment and atmosphere, and briefly denote the activities performed.

I rode an elevator to work, and met two gentlemen who were discussing the weather. The commute route that day and time was my context, where pleasantries were exchange regarding weather. The environmental atmosphere was neutral, as were each of are overall pleasant. The activities performed were basic, as it only involved a handful of sentences. Signup and view all the answers

How could you use chatGPT to measure your communications?

Chat bots, or other similar AI tools have been used to provide prompts to detect any feelings during an interaction. The bots can also be used to reflect on whether the user can identify with any generated responses. Signup and view all the answers

Describe the first step of using machine learnign alorithms in natural language processiing

The first step would be to do data exploration, such as gathering, cleaning for the model, before implementing a model. Signup and view all the answers

What are two common challenges for applying for machine learning algorithms for language.

Two common challenges when applying machine learning algorithms for language include handling different contextual references in many ways and word semantics. Signup and view all the answers

Describe one algorithm or data structure that reduces the computational overhead of applying machine learning models to natural language.

Stemming is a common algorithm that can be beneficial as it allows words to stem to their roots (eg. playing reduces to play) as the different words relate. Thus, many words can be simplified. Signup and view all the answers

Describe some examples that would be key metrics to understanding emotional states of a person

Many responses could be correct but one is galvanic skin repsonse, voice or speech patterns, facial expressions, etc. It is possible to relate emotional responses to these directly. Signup and view all the answers

How would you define "semantic understanding of language"?

Semantic understanding refers to the machine learning models understanding of language. Typically this understanding relies on the meaning and context from language that is related. Signup and view all the answers

How does a natural language processing model understand that "excellent customer service" means the same thing as "amazing customer service"?

The relationships that come from excellent and amazing as defined through the existing data and features helps define that both relate to a similar concept. Signup and view all the answers

When classifying data could there be biases? List out possible ways to check for these biases

Yes, biases can exist. To account for these look into how balanced the model is and perform stress test from data that you expect to fail. Signup and view all the answers

When attempting to test out natural language processing frameworks, should you load balance all the data from many source, or slowly load the data from many sources?

It is important to load the data from many sources slowly to account from different schema and data quality errors. Loading too much data from the starts can mean a complete fail to begin. Signup and view all the answers

Flashcards

What are emotions?

The effect of chemical messages released by our brain.

Categorical emotions

Representations that classify emotions into distinct groups, such as happiness, sadness, anger, fear, surprise, and disgust.