Naive Bayes Classifier Overview
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Questions and Answers

What is the primary assumption made by the Naive Bayes Classifier?

  • The features are conditionally independent given the class. (correct)
  • All features are equally important for classification.
  • The features are dependent given the class.
  • The order of words in a document is crucial for classification.

    • In the Bayes' Rule equation, P(d|c) represents the probability of a document belonging to a specific class.

    False (B)

    What is the primary reason for using logarithms when calculating probabilities in the Naive Bayes Classifier?

    To avoid floating-point underflow.

    The Naive Bayes Classifier uses the ______ assumption, which states that the order of words in a document does not affect its classification.

    <p>Bag of Words</p> Signup and view all the answers

    Match the following terms with their corresponding descriptions:

    <p>cMAP = The most likely class for a given document P(c) = Prior probability of a class P(d|c) = Likelihood of a document given a class P(c|d) = Posterior probability of a class given a document</p> Signup and view all the answers

    In the example provided, what is the probability of class C being 't'?

    <p>0.6 (D)</p> Signup and view all the answers

    The provided example demonstrates the application of Naive Bayes classification, where we calculate the probability of each class given the observed features.

    <p>True (A)</p> Signup and view all the answers

    What is the main challenge addressed by the sentiment classification with negation technique discussed in the text?

    <p>The changing of the meaning of words like 'like' to negative due to the presence of negations, such as 'don't' or 'didn't'.</p> Signup and view all the answers

    The technique of adding 'NOT_' to words between a negation and the following punctuation is a simple baseline method for addressing the challenge of ______ in sentiment analysis.

    <p>negation</p> Signup and view all the answers

    Match the following concepts with their relevant examples:

    <p>Naive Bayes = Calculating the probability of each class based on observed features Sentiment Classification with Negation = Addressing the changing meaning of words due to negation Lexicons = Pre-built word lists for sentiment analysis when labeled data is limited MPQA Subjectivity Cues Lexicon = An example of a publicly available lexicon for sentiment analysis</p> Signup and view all the answers

    Naive Bayes is a linear classifier because it uses a linear function of the inputs to make predictions.

    <p>True (A)</p> Signup and view all the answers

    What are the benefits of using Laplace smoothing in the Naive Bayes model?

    <p>It avoids zero probabilities, which are problematic for conditional probabilities. (B)</p> Signup and view all the answers

    How is the apriory probability of a class 'cj' calculated in the Naive Bayes model?

    <p>The apriory probability of a class 'cj' is calculated by dividing the number of documents belonging to that class 'Nc' by the total number of documents 'Ntotal' in the training set.</p> Signup and view all the answers

    A ______ is a document that concatenates all documents belonging to a specific topic.

    <p>mega-document</p> Signup and view all the answers

    Match the following terms to their appropriate descriptions:

    <p>Laplace Smoothing = A method to address the issue of zero probabilities in the Naive Bayes model. Mega-document = A combined document containing all documents belonging to a specific topic. Stop words = Common words that are often removed from text data because they hold little information for classification.</p> Signup and view all the answers

    Maximum likelihood estimation in Naive Bayes often leads to zero probabilities, which can be a problem for the model.

    <p>True (A)</p> Signup and view all the answers

    The probability of a word 'wk' appearing in a document belonging to class 'cj' is calculated by dividing the ______ of word 'wk' in the mega-document of topic 'cj' by the total number of words in the mega-document.

    <p>number of occurrences</p> Signup and view all the answers

    Why is it generally not helpful to build an unknown word model for Naive Bayes?

    <p>Knowing which class has more unknown words is not generally helpful in classifying documents.</p> Signup and view all the answers

    Which of these lexicons is specifically designed for analyzing sentiments expressed in social media?

    <p>VADER (C)</p> Signup and view all the answers

    The MPQA Subjectivity Lexicon is a free resource for research use.

    <p>True (A)</p> Signup and view all the answers

    What is the primary focus of the MPQA Subjectivity Lexicon?

    <p>The lexicon focuses on annotating words for their intensity (strong/weak) and their polarity (positive/negative).</p> Signup and view all the answers

    The General Inquirer categorizes words into ______ and ______ categories.

    <p>Positiv, Negativ</p> Signup and view all the answers

    Which of these is NOT a category covered by the General Inquirer?

    <p>Humor vs Seriousness (A)</p> Signup and view all the answers

    Match the lexicons with their primary focus:

    <p>MPQA Subjectivity Lexicon = Sentiment analysis of social media VADER = Identifying subjective words and their intensity The General Inquirer = Categorizing words into various psychological and linguistic dimensions None of the above =</p> Signup and view all the answers

    What does the symbol j represent in the set of model parameters ?

    <p>The probability of class c_j given the parameters  (D)</p> Signup and view all the answers

    The naïve Bayesian classifier assumes that the probability of a word is dependent on its position in the document.

    <p>False (B)</p> Signup and view all the answers

    What is the assumption made by the generative model regarding the document length?

    <p>The document length is chosen independently of its class.</p> Signup and view all the answers

    The probability of document d_i given the class c_j and model parameters  is denoted as [BLANK].

    <p>Pr(d_i | c_j; )</p> Signup and view all the answers

    Which of the following assumptions is NOT made by the naïve Bayesian classification model?

    <p>The probability of a word depends on its position in the document. (D)</p> Signup and view all the answers

    In the multinomial distribution, the number of independent trials corresponds to the length of the document.

    <p>True (A)</p> Signup and view all the answers

    What is the formula for calculating the probability of a document d_i given the model parameters ?

    <p>Pr(d_i | ) = _(j=1)^(|C|) Pr(c_j | ) Pr(d_i | c_j; )</p> Signup and view all the answers

    What is a main problem with the Naive Bayes algorithm when it is put to practice?

    <p>The mixture model assumption is often violated (B)</p> Signup and view all the answers

    Naïve Bayesian learning is always accurate.

    <p>False (B)</p> Signup and view all the answers

    What is one potential harm associated with sentiment classifiers, as highlighted in the text?

    <p>Sentiment classifiers can perpetuate negative stereotypes by assigning lower sentiment and more negative emotion to sentences containing certain names.</p> Signup and view all the answers

    Toxicity detection aims to identify hate speech, abuse, harassment, or other types of ____ language.

    <p>toxic</p> Signup and view all the answers

    Match the following concepts with their potential sources of error:

    <p>Sentiment Classifiers = Biases in training data Toxicity Classifiers = Overly strict detection of non-toxic sentences mentioning identities Naive Bayes Algorithm = Violation of the mixture model assumption</p> Signup and view all the answers

    Study Notes

    Sentiment Analysis

    • Sentiment analysis is the process of detecting attitudes.
    • A simple task is determining if the attitude of a text is positive or negative.

    Text Classification: Definition

    • Input: a document (d) and a set of classes (C).
    • Output: a predicted class (c) from the set of classes (C).

    Classification Methods: Supervised Machine Learning

    • Input: A document (d), a fixed set of classes (C), and a training set (m) of hand-labeled documents.
    • Output: A learned classifier (y:d → c)

    Classification Methods: Supervised Learning - Classifier Types

    • Naïve Bayes
    • Logistic regression
    • Neural networks
    • k-Nearest Neighbors

    Naive Bayes Intuition

    • A simple classification method based on Bayes' rule.
    • Uses a simple representation of a document (Bag of Words).

    Bag of Words Representation

    • A method for representing a document as a collection of words, without considering their order.

    Bayes' Rule Applied to Documents and Classes

    • Mathematically expresses the probability of a class given a document.

    Naive Bayes Classifier (I)

    • Maximizes the posterior probability (MAP).
    • Involves Bayes' rule and dropping the denominator.

    Naive Bayes Classifier (II)

    • The "likelihood" and "prior" components of the equation to estimate the posterior probability.

    Multinomial Naïve Bayes Independence Assumptions

    • There is no importance attached to word position.
    • Conditional probabilities of features (P(xi|cj)) are independent, given the class (c).

    Multinomial Naïve Bayes Classifier

    • Two mathematical forms expressing class maximization (MAP & CNB).

    Applying Multinomial Naive Bayes Classifiers to Text Classification

    • All word positions in a document are used.

    Problems with Multiplying Lots of Probabilities

    • Multiplying lots of probabilities can lead to floating-point underflow.
    • This is solved by using logs, as log(ab) = log(a) + log(b).

    We Actually Do Everything in Log Space

    • The ranking of classes remains the same if logs are used.

    Learning the Multinomial Naïve Bayes Model

    • Estimate probabilities of a class and word occurrences based on frequencies in the training data.

    Parameter Estimation

    • Use frequency counts to determine prior and conditional probabilities.

    Problem with Maximum Likelihood

    • If no training documents contain a word in a particular class, its conditional probability is zero.
    • A zero probability makes classification impossible.

    Laplace (add-1) Smoothing for Naïve Bayes

    • A method to address the issue of zero probabilities by adding one to all counts.

    Multinomial Naïve Bayes: Learning

    • Extract vocabulary from the training corpus,
    • Calculate class prior probabilities(P(cj)),
    • Calculate conditional probabilities(P(wk | cj)).

    Unknown Words

    • Handle unknown words by ignoring them in the test document.

    Stop Words

    • Remove frequent words (e.g., "the", "a") to reduce noise.

    Binary Multinomial Naïve Bayes: Learning

    • Calculate class prior and conditional probabilities for binary features (0, 1).

    Binary Multinomial Naïve Bayes on a Test Document d

    • Remove duplicates from the test document.
    • Use the same equations to compute the Naive Bayes classification.

    Binary Multinomial Naïve Bayes (Example)

    • Show that different counts can be possible, and example of binarization.

    An Example

    • Illustrative example of calculating probabilities for classification with a small dataset (10 examples & 2 classes)

    An Example (cont ...)

    • Calculations to show how probabilities are determined for the classes.

    Sentiment Analysis Example with Add-1 Smoothing

    • Calculation demonstration of the process with add-1 smoothing

    Optimizing for Sentiment Analysis

    • For sentiment, the occurrence of words is more important than word frequency.

    Binary Multinomial Naive Bayes : Learning (Example)

    • Algorithm example

    Naive Bayes in Other tasks: Spam Filtering

    • Uses features like the presence of many numbers and capital letters

    Naive Bayes in Language ID

    • Suitable for determining the language of text through character-based n-grams.

    Summary: Naive Bayes is Not So Naive

    • Offers high speed and low storage requirements.
    • Adaptable to smaller training data sets.

    Naive Bayes: Relationship to Language Modeling

    • Close relationship to language modeling.

    Generative Model for Multinomial Naïve Bayes (graphical example)

    • Illustrates a generative model that corresponds to Naive Bayes with a graphical representation.

    Naïve Bayes and Language Modeling

    • Explains Naive Bayes's ability to use standard language features.

    Each class = a unigram Language Model

    • Illustrates assigning probabilities to words in each class based on frequency for a simple sentence.

    Naive Bayes as a Language Model

    • Compares assigning likelihoods to a sentence using the classes of one language model.

    Probabilistic Framework

    • Defines the main ideas of the framework, such as generative models and their properties, like mixture models and the related correspondences with classes.

    Mixture Model

    • Describes a statistical model that combines multiple distributions.

    Mixture Model (cont ...)

    • Delves into the specific notation and components of the model.

    Document Generation

    • Show how Mixture models generate documents.

    Model Text Documents

    • Explains the method of treating texts as "bags of words."

    Multinomial Distribution

    • Details the mathematical concept of a multinomial distribution.

    Use Probability Function of Multinomial Distribution

    • Includes the mathematical formulations required to use the function.

    Parameter Estimation

    • Discusses the methods and formulas used for estimating parameters based on counts of data.

    Parameter Estimation II

    • Calculates class probabilities using training data.

    Classification

    • Discusses the process of classifying test documents based on calculations from previous steps

    Discussions

    • Summarizes the strengths and limitations of Naive Bayes, including its assumptions and efficiency.

    Harms in Sentiment Classifiers

    • Describes how existing classifiers can perpetuate negative stereotypes.

    Harms in Toxicity Classification

    • Highlights the potential for toxicity classifiers to incorrectly identify neutral or harmless content as toxic.

    What Causes These Harms?

    • Discusses the possible causes of biased classification.

    Model Cards

    • Explains the importance of documenting the details of an algorithm for responsible use.

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    Sentiment and Text Classification Techniques

    Description

    This quiz explores the fundamental concepts of the Naive Bayes Classifier, including its primary assumptions and applications in sentiment classification. Test your understanding of Bayes' Rule, probability calculations, and the role of logarithms in these processes.

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