1In the context of sentiment analysis using Logistic Regression, what is the primary purpose of feature extraction?
A.To increase the length of the sentences
B.To transform raw text into numerical representations like vectors
C.To translate text from one language to another
D.To convert numerical vectors back into text
Correct Answer: To transform raw text into numerical representations like vectors
Explanation:
Machine learning models require numerical input. Feature extraction converts raw text data into numerical vectors that the model can process.
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2Which function is used in Logistic Regression to map the output to a probability value between 0 and 1?
A.ReLU Function
B.Linear Function
C.Sigmoid Function
D.Tangent Function
Correct Answer: Sigmoid Function
Explanation:
The sigmoid function maps any real-valued number into a value between 0 and 1, making it suitable for probability estimation in binary classification.
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3When extracting features for sentiment analysis, what does a frequency dictionary typically map?
A.Each word to its synonym
B.Process ID to Memory usage
C.Word length to Sentence length
D.(Word, Sentiment Label) pairs to the count of occurrences
Correct Answer: (Word, Sentiment Label) pairs to the count of occurrences
Explanation:
In simple feature extraction for sentiment analysis, a frequency dictionary maps a pair (word, label) to the number of times that word appears in the corpus associated with that specific label.
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4In a binary logistic regression classifier for sentiment analysis, if the sigmoid output h(x) >= 0.5, how is the sentiment classified?
A.Positive
B.Undefined
C.Neutral
D.Negative
Correct Answer: Positive
Explanation:
By convention in binary classification, a probability threshold of 0.5 is used. If the output probability is 0.5 or greater, the class is predicted as Positive (1).
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5What is the formula for the sigmoid function, σ(z)?
A.log(z)
B.1 / (1 - e^-z)
C.1 / (1 + e^-z)
D.e^z / (1 + e^z)
Correct Answer: 1 / (1 + e^-z)
Explanation:
The standard formula for the sigmoid logistic function is 1 / (1 + e^-z).
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6In the feature vector representation X = [1, sum_pos, sum_neg], what does the '1' usually represent?
A.The learning rate
B.The classification threshold
C.The bias unit (intercept term)
D.The count of the first word
Correct Answer: The bias unit (intercept term)
Explanation:
The '1' is added to the feature vector to correspond to the bias weight (theta_0) in the dot product calculation.
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7Which preprocessing step is commonly performed before feature extraction to reduce the vocabulary size without losing semantic meaning?
A.Removing all vowels
B.Stemming and removing stop words
C.Duplicating sentences
D.Capitalizing all letters
Correct Answer: Stemming and removing stop words
Explanation:
Stemming reduces words to their root form (e.g., 'tuning' to 'tun'), and removing stop words eliminates common non-informative words, both reducing vocabulary size.
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8What is the 'Cost Function' used in Logistic Regression generally called?
A.Absolute Error
B.Hinge Loss
C.Mean Squared Error
D.Cross-Entropy Loss (Log Loss)
Correct Answer: Cross-Entropy Loss (Log Loss)
Explanation:
Logistic regression uses the Cross-Entropy (or Log Loss) function because it is convex, allowing gradient descent to find the global minimum.
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9What is the goal of Gradient Descent in training a Logistic Regression model?
A.To remove the bias term
B.To minimize the cost function by iteratively updating weights
C.To maximize the cost function
D.To set all weights to zero
Correct Answer: To minimize the cost function by iteratively updating weights
Explanation:
Gradient Descent is an optimization algorithm used to minimize the cost function by adjusting parameters (weights) in the opposite direction of the gradient.
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10In Bayes' Rule, what does P(A|B) represent?
A.The joint probability of A and B
B.The posterior probability of A given B
C.The probability of B given A
D.The prior probability of A
Correct Answer: The posterior probability of A given B
Explanation:
P(A|B) is the conditional probability of event A occurring given that event B is true, often called the posterior.
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11Why is the Naive Bayes classifier called 'Naive'?
A.It cannot handle complex text
B.It requires very little training data
C.It was developed by a naive mathematician
D.It assumes that features are independent of each other given the class
Correct Answer: It assumes that features are independent of each other given the class
Explanation:
The 'naive' assumption is that the occurrence of a particular word is independent of the occurrence of other words, given the sentiment label.
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12What is the formula for Bayes' Theorem?
A.P(A|B) = P(A) / P(B)
B.P(A|B) = P(B|A) + P(A)
C.P(A|B) = P(A) * P(B)
D.P(A|B) = P(B|A) * P(A) / P(B)
Correct Answer: P(A|B) = P(B|A) * P(A) / P(B)
Explanation:
Bayes' theorem states that the posterior probability is the likelihood times the prior divided by the evidence.
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13In Naive Bayes Sentiment Analysis, what is 'Laplacian Smoothing' used for?
A.To average the sentiment scores
B.To smooth the decision boundary
C.To remove stop words
D.To handle words with zero probability (words not seen in training)
Correct Answer: To handle words with zero probability (words not seen in training)
Explanation:
Laplacian smoothing adds a small count (usually 1) to all frequency counts to prevent the probability of an unseen word becoming zero, which would zero out the entire calculation.
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14If a word appears in the positive corpus but not the negative corpus, what happens to its probability P(W|Negative) without smoothing?
A.It becomes 0
B.It becomes 0.5
C.It becomes 1
D.It becomes infinity
Correct Answer: It becomes 0
Explanation:
Without smoothing, the frequency count is 0, so the probability calculation results in 0.
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15In Logistic Regression, if the dot product θ^T * x is 0, what is the output of the sigmoid function?
A.undefined
B.0
C.0.5
D.1
Correct Answer: 0.5
Explanation:
e^0 is 1. The sigmoid function is 1/(1+1) = 0.5. This is typically the decision boundary.
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16Which of the following describes the 'Prior Probability' P(Positive) in Naive Bayes?
A.Probability of a word being positive
B.Total number of words in the dictionary
C.Probability of a document being positive based on the training set distribution
D.Probability of a document being positive given a specific word
Correct Answer: Probability of a document being positive based on the training set distribution
Explanation:
The prior P(Positive) is the ratio of the number of positive documents to the total number of documents in the training set.
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17Why do we typically use Log Likelihood in Naive Bayes calculations instead of raw probabilities?
A.Because logarithms are faster to compute than addition
B.To convert negative numbers to positive
C.To make the calculation harder
D.To prevent numerical underflow from multiplying many small probabilities
Correct Answer: To prevent numerical underflow from multiplying many small probabilities
Explanation:
Multiplying many small probabilities (between 0 and 1) results in extremely small numbers that computers process as zero (underflow). Using logs converts multiplication to addition.
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18In the Naive Bayes inference formula, if the sum of the Log Prior and Log Likelihoods is greater than 0, the sentiment is classified as:
A.Positive
B.Negative
C.Neutral
D.Ambiguous
Correct Answer: Positive
Explanation:
The Log Likelihood ratio is typically defined as log(P(W|Pos)/P(W|Neg)). If the total sum including the log prior is > 0, the positive probability outweighs the negative.
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19What is a 'sparse representation' in the context of NLP feature vectors?
A.A vector with mostly non-zero values
B.A vector where most elements are zero
C.A vector containing only negative numbers
D.A vector with a small dimension
Correct Answer: A vector where most elements are zero
Explanation:
When using One-Hot Encoding or large vocabulary counts, most words in the vocabulary do not appear in a single sentence, resulting in a vector mostly filled with zeros.
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20Which component of the Naive Bayes classifier represents the 'Evidence' in Bayes' rule?
A.P(Data | Class)
B.P(Data)
C.P(Class | Data)
D.P(Class)
Correct Answer: P(Data)
Explanation:
The denominator P(Data) represents the evidence or marginal likelihood. In classification, it is often ignored as it is constant for all classes.
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21In Logistic Regression, what is the dimension of the weight vector θ if the feature vector x has dimension V+1?
A.V*2
B.1
C.V+1
D.V
Correct Answer: V+1
Explanation:
To perform the dot product θ^T * x, the weight vector must have the same dimensions as the feature vector.
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22Which algorithm is considered a 'Generative' model?
A.Perceptron
B.Logistic Regression
C.Support Vector Machine
D.Naive Bayes
Correct Answer: Naive Bayes
Explanation:
Naive Bayes is a generative model because it models the joint probability distribution P(x, y) (how the data is generated), whereas Logistic Regression is discriminative.
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23Which algorithm is considered a 'Discriminative' model?
A.Naive Bayes
B.Hidden Markov Model
C.Gaussian Mixture Model
D.Logistic Regression
Correct Answer: Logistic Regression
Explanation:
Logistic Regression is discriminative because it directly models the conditional probability P(y|x) (the boundary between classes).
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24What is the 'Lambda' (λ) term in the context of Naive Bayes ratio calculation?
A.The learning rate
B.The bias unit
C.The smoothing parameter
D.The number of classes
Correct Answer: The smoothing parameter
Explanation:
Lambda is the additive smoothing parameter (Laplacian smoothing) added to the numerator and denominator of probability calculations.
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25When extracting features for Logistic Regression, if the word 'happy' appears 3 times in a tweet, and 'happy' has a positive frequency of 100 and negative frequency of 5 in the corpus, how is this typically utilized?
A.The number 3 is the only feature used
B.The ratio 100/5 is used as the weight
C.The counts 100 and 5 contribute to the aggregate sums in the feature vector
D.The word is ignored
Correct Answer: The counts 100 and 5 contribute to the aggregate sums in the feature vector
Explanation:
In standard frequency-based feature extraction, we look up the pre-computed corpus frequencies (100, 5) and sum them into the feature vector slots for the tweet.
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26What is the main advantage of Logistic Regression over Naive Bayes?
A.It is always faster to train
B.It handles missing data better
C.It does not require independent features
D.It is a generative model
Correct Answer: It does not require independent features
Explanation:
Logistic Regression learns the weights of features based on their correlation with the output and does not strictly assume feature independence like Naive Bayes.
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27In the context of Naive Bayes, what is V?
A.The vector dimension
B.The validation set size
C.The number of classes
D.The vocabulary size (number of unique words)
Correct Answer: The vocabulary size (number of unique words)
Explanation:
V usually denotes the size of the vocabulary, which is used in the denominator of the smoothed probability formula.
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28If the learning rate in Logistic Regression is too large, what might happen?
A.The model may overshoot the minimum and fail to converge
B.The model will always find the global minimum
C.The cost function becomes 0 immediately
D.The model converges very slowly
Correct Answer: The model may overshoot the minimum and fail to converge
Explanation:
A large learning rate causes the gradient descent steps to be too big, potentially bouncing over the minimum cost and diverging.
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29Which of the following is a hyperparameter in Logistic Regression?
A.The learning rate α
B.The bias term
C.The weight vector θ
D.The feature vector x
Correct Answer: The learning rate α
Explanation:
The learning rate is set before training begins and controls the step size; weights and bias are parameters learned during training.
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30How is the 'Log Prior' calculated for the positive class?
A.log(N_pos / N_neg)
B.log(N_neg / N_pos)
C.log(V)
D.log(N_pos / N_total)
Correct Answer: log(N_pos / N_neg)
Explanation:
In the log-likelihood ratio formulation of Naive Bayes, the log prior is the log of the ratio of the number of positive documents to negative documents.
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31What is the range of values for the output of the standard Naive Bayes probability calculation P(y|x) before applying logs?
A.(-infinity, +infinity)
B.[-1, 1]
C.[0, 1]
D.[0, 100]
Correct Answer: [0, 1]
Explanation:
Probabilities are always between 0 and 1.
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32Which sentiment lexicon is purely generated from the training data in the approaches discussed?
A.The Lambda dictionary (Log Likelihood ratios of words)
B.WordNet
C.Google Dictionary
D.SentiWordNet
Correct Answer: The Lambda dictionary (Log Likelihood ratios of words)
Explanation:
In the Naive Bayes approach, we build a dictionary of lambda values (log likelihood ratios) for each word based specifically on the frequencies observed in the training corpus.
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33When predicting with Naive Bayes, if a word in the test sentence is not in the training vocabulary (V), what is the standard action?
A.Discard it (ignore it)
B.Re-train the model
C.Halt the program
D.Assign it a random probability
Correct Answer: Discard it (ignore it)
Explanation:
Words not in the vocabulary do not have a computed likelihood ratio and typically contribute 0 to the log-sum score (i.e., they are ignored).
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34What is 'Sentiment Analysis' primarily classifying?
A.The emotional tone or opinion (e.g., Positive/Negative)
B.The grammatical structure
C.The language of the text
D.The topic of the text
Correct Answer: The emotional tone or opinion (e.g., Positive/Negative)
Explanation:
Sentiment analysis is the computational study of opinions, sentiments, and emotions expressed in text.
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35In Logistic Regression, the decision boundary is:
A.Linear
B.Circular
C.Non-linear
D.Polynomial
Correct Answer: Linear
Explanation:
Logistic regression creates a linear decision boundary (a line or hyperplane) that separates the classes.
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36The denominator for calculating P(w|class) with Laplacian smoothing is:
A.Count(w in class) + 1
B.V
C.N_class (total words in class) + V (vocabulary size)
D.N_class
Correct Answer: N_class (total words in class) + V (vocabulary size)
Explanation:
To normalize the smoothed counts, we divide by the total number of words in the class plus the size of the vocabulary (since we added 1 for every unique word).
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37What does a negative value in a word's Log Likelihood (Lambda) score imply in Sentiment Analysis?
A.The word is neutral
B.The word is indicative of Negative sentiment
C.The word is indicative of Positive sentiment
D.The word is a stop word
Correct Answer: The word is indicative of Negative sentiment
Explanation:
Lambda is log(P(w|Pos)/P(w|Neg)). If P(w|Neg) > P(w|Pos), the ratio is < 1, and the log is negative.
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38Which of the following is a stop word?
A.The
B.Amazing
C.Terrible
D.Love
Correct Answer: The
Explanation:
'The' is a common article with little semantic sentiment value, classifying it as a stop word.
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39In a confusion matrix for binary classification, what is a False Positive?
A.Incorrectly predicting negative when the actual class is positive
B.Correctly predicting positive
C.Correctly predicting negative
D.Incorrectly predicting positive when the actual class is negative
Correct Answer: Incorrectly predicting positive when the actual class is negative
Explanation:
A False Positive is an error where the model predicts the positive class, but the ground truth was actually negative.
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40What is the primary reason for lowercasing text during preprocessing?
A.To ensure 'Good' and 'good' are treated as the same feature
B.To remove punctuation
C.To detect sentence boundaries
D.It looks better
Correct Answer: To ensure 'Good' and 'good' are treated as the same feature
Explanation:
Lowercasing normalizes the text so that capitalization variations do not create duplicate features in the vocabulary.
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41If a Logistic Regression model is overfitted, how will it perform?
A.Well on training data, well on test data
B.Poorly on training data, well on test data
C.Poorly on training data, poorly on test data
D.Well on training data, poorly on test data
Correct Answer: Well on training data, poorly on test data
Explanation:
Overfitting occurs when the model learns the noise in the training data, resulting in high accuracy on training but poor generalization to unseen test data.
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42Which formula represents the update rule for weight θ_j in Gradient Descent?
A.θ_j := dJ/dθ_j
B.θ_j := θ_j + α * dJ/dθ_j
C.θ_j := θ_j / α
D.θ_j := θ_j - α * dJ/dθ_j
Correct Answer: θ_j := θ_j - α * dJ/dθ_j
Explanation:
We subtract the product of the learning rate and the partial derivative of the cost function to move down the gradient.
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43In Naive Bayes, what assumption allows us to multiply probabilities of individual words?
A.Homoscedasticity
B.Conditional Independence
C.Normal Distribution
D.Linear Separability
Correct Answer: Conditional Independence
Explanation:
The Naive Bayes assumption is that the probability of feature xi depends only on the class y, independent of other features xj.
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44What is Tokenization?
A.Converting words to numbers
B.Splitting a string of text into individual words or terms
C.Removing special characters
D.Translating text
Correct Answer: Splitting a string of text into individual words or terms
Explanation:
Tokenization is the process of breaking a stream of text up into words, phrases, symbols, or other meaningful elements called tokens.
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45Which value of probability corresponds to the logit (log-odds) value of 0?
A.0
B.Infinity
C.1
D.0.5
Correct Answer: 0.5
Explanation:
Log-odds = log(p / (1-p)). If p=0.5, then 0.5/0.5 = 1, and log(1) = 0.
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46What is the interpretation of weights in Logistic Regression for Sentiment Analysis?
A.They represent the frequency of words
B.They represent the importance and direction (positive/negative) of a feature
C.They are random numbers
D.They represent the index of the word
Correct Answer: They represent the importance and direction (positive/negative) of a feature
Explanation:
A high positive weight implies the feature strongly correlates with the positive class; a high negative weight implies correlation with the negative class.
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47For a balanced dataset, which metric is most straightforward to evaluate performance?
A.Recall only
B.Accuracy
C.Mean Squared Error
D.Precision only
Correct Answer: Accuracy
Explanation:
Accuracy (Correct Predictions / Total Predictions) is a good metric when the classes in the dataset are balanced (e.g., 50% positive, 50% negative).
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48In vector space models, what is 'Oov'?
A.Over optimization value
B.Out of vocabulary
C.Object oriented vector
D.Out of vector
Correct Answer: Out of vocabulary
Explanation:
OOV stands for Out-Of-Vocabulary, referring to words encountered in testing that were not present in the training vocabulary.
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49The conditional probability P(Word|Positive) is conceptually similar to:
A.The probability that the sentiment is Positive given the word
B.How often the word appears in the whole dataset
C.The probability of the word being a stop word
D.How likely the word is to appear if the sentiment is known to be Positive
Correct Answer: How likely the word is to appear if the sentiment is known to be Positive
Explanation:
This is the likelihood: given the class is Positive, what is the probability of observing this specific word.
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50Which technique is essentially a 'probabilistic classifier' based on applying Bayes' theorem?
A.K-Nearest Neighbors
B.K-Means
C.Naive Bayes
D.Decision Trees
Correct Answer: Naive Bayes
Explanation:
Naive Bayes is explicitly founded on Bayes' theorem to calculate probabilities for classification.
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