Virtual Labs

Long Short-Term Memory (LSTM) for Sentiment Analysis

1. What is Sentiment Analysis?

a: Identifying the genre of a movie Explanation

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b: Detecting emotions or opinions expressed in text Explanation

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c: Translating text from one language to another Explanation

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d: Compressing text documents Explanation

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2. In the IMDB dataset used in this experiment, what does a label of 1 indicate?

a: Neutral sentiment Explanation

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b: Negative review Explanation

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c: Positive review Explanation

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d: Unlabelled data Explanation

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3. Why are movie reviews challenging for sentiment analysis?

a: Long-range dependencies and negations affect meaning Explanation

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b: Sentiment depends only on individual words Explanation

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c: Reviews always have a fixed length Explanation

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d: Reviews contain only numerical data Explanation

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4. What is the main limitation of a simple Recurrent Neural Network (RNN)?

a: It cannot process sequential data Explanation

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b: It suffers from vanishing and exploding gradient problems Explanation

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c: It requires labelled image data Explanation

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d: It cannot be trained using backpropagation Explanation

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5. What is the primary purpose of gates in an LSTM network?

a: To increase the number of neurons Explanation

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b: To randomly drop connections Explanation

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c: To convert text into numerical vectors Explanation

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d: To control what information is stored, forgotten, and output Explanation

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6. Which dataset is commonly used for benchmarking sentiment analysis models?

a: MNIST Explanation

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b: CIFAR-10 Explanation

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c: IMDB Movie Reviews Dataset Explanation

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d: ImageNet Explanation

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7. As formulated in this experiment, sentiment analysis is an example of which type of learning?

a: Unsupervised learning Explanation

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b: Reinforcement learning Explanation

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c: Supervised learning Explanation

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d: Self-supervised learning Explanation

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8. What does the term “binary classification” mean in this experiment?

a: Classifying data into two distinct classes Explanation

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b: Predicting numerical values Explanation

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c: Classifying text into multiple categories Explanation

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d: Clustering text documents Explanation

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9. Why is word order important in sentiment analysis?

a: Because models ignore punctuation Explanation

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b: Because sentiment depends on context and the sequence of words Explanation

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c: Because reversing words does not change meaning Explanation

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d: Because all words have equal importance Explanation

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10. Why is the IMDB dataset suitable for LSTM-based sentiment analysis?

a: It contains only short sentences Explanation

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b: It is balanced and contains long text sequences Explanation

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c: It has image and text data combined Explanation

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d: It requires no preprocessing Explanation

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