Movie Review Sentiment Analysis Quiz

<p>Apply 'forest.predict' on the test data’s features. (A)</p> Signup and view all the answers

<p>.csv or .xlsx (C)</p> Signup and view all the answers

<p>A negative review with a rating less than 5. (B)</p> Signup and view all the answers

<p>To ignore double quotes and prevent errors during reading (C)</p> Signup and view all the answers

<p>1 (A)</p> Signup and view all the answers

<p>To extract text from HTML content. (B)</p> Signup and view all the answers

<p>The file uses tab characters as separators between the fields. (B)</p> Signup and view all the answers

<p>Punctuation can carry sentiment-related information. (C)</p> Signup and view all the answers

<p>25,000 (A)</p> Signup and view all the answers

<p>To remove punctuation and numbers from the text. (C)</p> Signup and view all the answers

<p>To replace all non-alphabetic characters with spaces. (C)</p> Signup and view all the answers

<p>Splitting text into individual words. (A)</p> Signup and view all the answers

<p>A frequently occurring word that typically doesn't carry much meaning. (B)</p> Signup and view all the answers

<p>Searching sets in Python is faster than searching lists. (B)</p> Signup and view all the answers

<p>To make the output more suitable for use in the Bag of Words approach. (C)</p> Signup and view all the answers

<p>Converting to lowercase, and splitting into words (Tokenization). (A)</p> Signup and view all the answers

<p>It converts the processed word back to a paragraph string. (A)</p> Signup and view all the answers

<p>To make the code reusable for cleaning multiple reviews. (D)</p> Signup and view all the answers

<p>It learns a vocabulary from all provided documents, then quantifies word occurrences in each. (C)</p> Signup and view all the answers

<p>{ 2, 1, 1, 1, 1, 0, 0, 0 } (D)</p> Signup and view all the answers

<p>To limit the length of feature vectors and control the dimensionality. (B)</p> Signup and view all the answers

<p>It automatically performs preprocessing, tokenization, and stop word removal. It then translates the text into a numeric representation (matrix.) (A)</p> Signup and view all the answers

<p>{ 2, 1, 0, 1, 1} (B)</p> Signup and view all the answers

<p>{3, 2, 1, 0, 0, 0, 1, 0} (D)</p> Signup and view all the answers

<p>Splitting the text into sentences. (B)</p> Signup and view all the answers

<p>They are converted into numerical representations for machine learning. (B)</p> Signup and view all the answers

A collection of reviews with a positive or negative sentiment label.

A set of reviews used to evaluate the performance of a trained sentiment analysis model.

A unique identifier assigned to each movie review in the dataset.

A numerical value representing the sentiment of a movie review, typically 0 for negative and 1 for positive.

The text content of a movie review.

The process of removing irrelevant or noisy data from the text, such as HTML tags or punctuation, to prepare it for analysis.

A library used to remove HTML markup from text. It helps clean text by extracting only the actual content.

A Python library used for regular expression operations. It helps remove punctuation and numbers from text.

The process of converting textual data into numerical features suitable for machine learning algorithms.

A machine learning technique that represents text as a collection of words and their frequencies, ignoring word order.

A type of machine learning classifier that uses an ensemble of decision trees to make predictions.

A set of reviews used to evaluate the performance of a trained model after it has been trained on the training data.

The process of using a trained model to predict the sentiment (positive or negative) of unseen reviews.

A regular expression (regex) statement that finds and replaces characters that are not lowercase or uppercase letters with a space.

The process of breaking down text into individual words or units.

Words that occur frequently in a language but don't carry much meaning, such as "a", "and", "is", and "the".

A Python library offering a collection of tools for natural language processing, including a list of stop words.

The process of removing stop words from text.

A technique used to group words and their frequencies into a vector, representing the content of the text.

A function that can be reused to clean multiple reviews.

A collection of unique words found in a set of documents. It defines the vocabulary used to represent texts as numerical features.

A numerical representation of a document, where each element corresponds to the count of a particular word in the document's vocabulary.

A technique used to reduce the dimensionality of feature vectors by selecting only the most frequently occurring words, thereby creating a more manageable representation of text data.

A method for creating features from text data using word frequencies, implemented in the scikit-learn library.

A library in Python that offers a wide range of tools for text processing, including tokenization, stop word removal, and stemming.