Podcast
Questions and Answers
What is the dimension of the vector used to represent each tweet after feature extraction based on frequencies?
What is the dimension of the vector used to represent each tweet after feature extraction based on frequencies?
What is the primary goal of supervised machine learning in the context of the provided content?
What is the primary goal of supervised machine learning in the context of the provided content?
Which of the following words is likely to have a higher frequency in negative tweets?
Which of the following words is likely to have a higher frequency in negative tweets?
What is the role of the cost function in supervised machine learning?
What is the role of the cost function in supervised machine learning?
Signup and view all the answers
In the frequency dictionary, what does the number '23' represent in the context of the word 'followfriday'?
In the frequency dictionary, what does the number '23' represent in the context of the word 'followfriday'?
Signup and view all the answers
Which of the following accurately describes the process of sentiment analysis in the context of the provided text?
Which of the following accurately describes the process of sentiment analysis in the context of the provided text?
Signup and view all the answers
What is the purpose of creating a frequency dictionary in this context?
What is the purpose of creating a frequency dictionary in this context?
Signup and view all the answers
Given the feature vector [1, 8, 11] for the tweet "I am sad, I am not learning NLP", what does the number '8' represent?
Given the feature vector [1, 8, 11] for the tweet "I am sad, I am not learning NLP", what does the number '8' represent?
Signup and view all the answers
What is the primary challenge associated with using sparse vectors in sentiment analysis?
What is the primary challenge associated with using sparse vectors in sentiment analysis?
Signup and view all the answers
What is the significance of the vocabulary size (V) in sentiment analysis?
What is the significance of the vocabulary size (V) in sentiment analysis?
Signup and view all the answers
What is the primary benefit of using a large corpus for building a sentiment analysis vocabulary?
What is the primary benefit of using a large corpus for building a sentiment analysis vocabulary?
Signup and view all the answers
Which of the following is a common technique for reducing the dimensionality of sparse vectors in sentiment analysis?
Which of the following is a common technique for reducing the dimensionality of sparse vectors in sentiment analysis?
Signup and view all the answers
Why is it important to consider the trade-off between training time and prediction time when designing a sentiment analysis model?
Why is it important to consider the trade-off between training time and prediction time when designing a sentiment analysis model?
Signup and view all the answers
What is the first preprocessing step mentioned in the text?
What is the first preprocessing step mentioned in the text?
Signup and view all the answers
Which of the following is NOT a stop word mentioned in the text?
Which of the following is NOT a stop word mentioned in the text?
Signup and view all the answers
What does the text suggest is the output after all the preprocessing steps have been applied?
What does the text suggest is the output after all the preprocessing steps have been applied?
Signup and view all the answers
What is the purpose of stemming?
What is the purpose of stemming?
Signup and view all the answers
What is the dataset used for the sentiment analysis example?
What is the dataset used for the sentiment analysis example?
Signup and view all the answers
Why would it be an issue to use all 20,000 tweets from the 'twitter_samples' dataset for the sentiment analysis?
Why would it be an issue to use all 20,000 tweets from the 'twitter_samples' dataset for the sentiment analysis?
Signup and view all the answers
What is the purpose of the 'utils.py' file mentioned in the text?
What is the purpose of the 'utils.py' file mentioned in the text?
Signup and view all the answers
What is the percentage of data used for the test set in the sentiment analysis example?
What is the percentage of data used for the test set in the sentiment analysis example?
Signup and view all the answers
What is the main purpose of the process_tweet() function?
What is the main purpose of the process_tweet() function?
Signup and view all the answers
What does the build_freqs() function output?
What does the build_freqs() function output?
Signup and view all the answers
How does the extract_features() function utilize the freqs dictionary?
How does the extract_features() function utilize the freqs dictionary?
Signup and view all the answers
What type of data structure is the freqs dictionary?
What type of data structure is the freqs dictionary?
Signup and view all the answers
What does the sigmoid function accomplish in the context of logistic regression?
What does the sigmoid function accomplish in the context of logistic regression?
Signup and view all the answers
What is the crucial step taken by build_freqs() in constructing the frequency dictionary?
What is the crucial step taken by build_freqs() in constructing the frequency dictionary?
Signup and view all the answers
What is the output of the extract_features() function after processing a tweet?
What is the output of the extract_features() function after processing a tweet?
Signup and view all the answers
Which statement accurately describes the role of stemming in process_tweet()?
Which statement accurately describes the role of stemming in process_tweet()?
Signup and view all the answers
Study Notes
Web and Text Analytics 2024-25, Week 5
- The course covers web and text analytics.
- The presenter's name and website are provided.
- The information systems lab website and the university are also referenced.
- The material focuses on supervised machine learning, sentiment analysis, and feature extraction.
Supervised Machine Learning
- Supervised machine learning uses input (X) to predict output (Y^).
- The prediction is compared to the true value (Y).
- The difference (cost) is used to update parameters (θ).
- Features (X) are inputs to the system which then uses a prediction function to produce an Output (Y). This output is then compared to the actual value of the Output (Y) to calculate the error which is then used to update the parameters of the system
- Parameters are variables that control the prediction function.
Sentiment Analysis
- Sentiment analysis classifies text (e.g., tweets) as positive or negative (1 or 0) using an algorithm.
- A tweet like "I am happy because I am learning NLP" is classified as positive (1).
- Logistic regression is one classification algorithm used for sentiment analysis.
Supervised ML & Sentiment Analysis
- Representing text as features is the first step in sentiment analysis.
- Logistic regression is an example of a classifier.
- Sentiment is classified as either 1 (positive) or 0 (negative).
Vocabulary & Feature Extraction
- A vocabulary is a collection of words.
- A tweet can be represented as a vector based on the vocabulary.
- Vocabulary size (V) determines the vector dimension.
- The vocabulary is generated from all tweets in a corpus.
Feature Extraction
- In feature extraction, a 1 is placed in the index corresponding to a word in the tweet; otherwise, 0 is used.
- Tweets are encoded as vectors representing words.
- The vocabulary is created using all tweets within a corpus; this vocabulary might consist of potentially millions of words.
Sparse Vectors
- As the vocabulary size (V) increases, the feature vectors become sparser.
- The training and prediction time of the system increase as the number of features increase.
Feature Extraction with Frequencies
- Given a corpus of positive and negative tweets, a vocabulary is constructed.
- The frequency of each word in each sentiment class is recorded.
Positive and Negative Tweets
- The corpus contains positive and negative tweets.
- Positive tweets are specifically identified.
- Negative tweets are specifically identified.
Feature Extraction with Frequencies (Implementation)
- To encode each tweet, a dictionary mapping words to their frequency in each sentiment class (positive or negative) is created.
- The frequency of a word within each class determines the features for a given tweet.
Positive Counts
- Positive count dictionary is determined by positive tweets.
Negative Counts
- Negative count dictionary is determined by negative tweets.
Frequency Dictionary
- Words like "happy" and "sad" are characteristically associated with positive and negative sentiments respectively — their frequencies are significantly greater in either the positive or negative sets.
- Words like "I" and "am" are frequently present in both positive and negative tweets, and are accordingly less characteristic of either sentiment.
Feature Extraction
- Based on the frequency dictionary, features for each tweet are calculated.
Preprocessing: Stop Words and Punctuation
- Stop words and punctuation are removed from tweets before calculating feature vectors.
- Stop words (e.g., "and", "is") and punctuation (e.g., ".", "!") are eliminated.
Preprocessing: Handles and URLs
- Handles (@mentions) and URLs are removed from tweets.
Preprocessing: Stemming and Lowercasing
- Words are stemmed (reduced to their root form) and converted to lowercase.
- Stemming converts "tuning" to "tun".
- Lowercasing converts "GREAT" to "great".
Preprocessing
- The initial tweet is processed to generate a list of tokens.
- The tweet is preprocessed according to the mentioned steps before feature extraction and the associated values are calculated.
Preprocessing and Feature Extraction (Initial Case)
- Preprocessing steps are applied to the initial tweet.
- Preprocessed words and associated frequency values are stored.
Features Extraction
- Features are extracted for each tweet.
- Features are listed as a vector with a bias term.
Logistic Regression
- The sigmoid function maps 'z' to a probability value (between 0 and 1).
- The implementation details of logistic regression are explained in Part 1 of the notebook provided.
Create the Feature Matrix (X)
- A matrix (X) is constructed from tweet features.
- The
extract_features()function is used to obtain features from tweets.
Implement the extract_features Function
- The
extract_featuresfunction processes single tweets to extract feature vectors.
Create the feature Matrix (x) and train the model
- A feature matrix
Xis generated from the training set. - The
LogisticRegressionmodel is trained from the data in the feature matrixXwith correspondingYlabel data.
Check Performance Using the Test Set
- The model's performance is evaluated using a held-out test set.
- Accuracy, precision, recall, and F1-score are calculated.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Related Documents
Description
This quiz focuses on supervised machine learning and sentiment analysis as part of the Web and Text Analytics course for 2024-25. It covers key concepts such as feature extraction and the prediction function, providing a foundational understanding of how algorithms classify data. Test your knowledge on these important topics in data analytics.
