Big Data for Marketing Lecture 4

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Questions and Answers

What is one application of text classification mentioned in the content?

spam filtering

What technique involves breaking down text into a collection of words and disregarding grammar and word order?

Bag of Words (BoW) model

In the Bag of Words model, after tokenization and vocabulary building, what is the final step?

Vectorization

What are some challenges mentioned in the text regarding representing multiple words together?

Sparsity Signup and view all the answers

Stemming is a preprocessing technique that involves expanding words to their root form.

False Signup and view all the answers

What does part-of-speech tagging involve?

labeling each token with the appropriate tag Signup and view all the answers

What is sentiment analysis also known as?

opinion mining Signup and view all the answers

Valence Aware Dictionary for sEntiment Reasoning (VADER) is an example of a __________.

rule-based algorithm Signup and view all the answers

Which technology captures word relationships using a co-occurrence matrix?

GloVe Signup and view all the answers

What is the trade-off offered by distributed databases?

scalability Signup and view all the answers

Latent Dirichlet Allocation (LDA) is a generative probabilistic model used for sentiment analysis.

False Signup and view all the answers

According to the CAP Theorem, what are the three aspects that can be selected?

Availability Signup and view all the answers

Match the measure for document similarity with its description:

Jaccard Distance = A measure for document similarity Edit distance = Another measure for document similarity TF-IDF = Enhances Bag of Words model by weighing terms based on importance Signup and view all the answers

NoSQL databases do not have to be write-consistent all the time.

True Signup and view all the answers

NoSQL databases allow more tailored database designs with an emphasis on application logic and optimization patterns, shifting from hard design rules to __________ designs.

flexible Signup and view all the answers

What is the Hadoop Ecosystem known for?

big data processing Signup and view all the answers

What is Apache Spark primarily used for?

Large-scale data processing Signup and view all the answers

Study Notes

Text Mining and Natural Language Processing

Text classification: assigns a discrete label to a text document, where the set of possible labels is Y.
Applications: spam filtering, analysis of electronic health records, sentiment analysis, and more.

Bag of Words Model

Simplifies text by breaking it down into a collection of words, disregarding grammar and word order, but maintaining multiplicity (frequency of each word's appearance).
Steps:
- Tokenization: breaks text into individual words or tokens.
- Vocabulary building: creates a list of unique words.
- Vectorization: converts text into numerical vectors.

Tokenization

Types of tokens:
- Word tokens: most common type, e.g., ["Natural", "Language", "Processing", "is", "fascinating"].
- Subword tokens: useful for languages with rich morphology, e.g., "unhappiness" -> ["un", "happiness"].
- Character tokens: beneficial for languages with large character sets, e.g., "你好" -> ["你", "好"].
- Sentence tokens: treats entire sentences as tokens, e.g., sentence segmentation.

Challenges

Handling multiple words together (n-grams).
Dealing with punctuation, which can affect word meaning.
Stemming: reduces words to their root form, e.g., "running", "runner", and "ran" -> "run".

Part-of-Speech Tagging

Labels each token with its part of speech, encoding information about the word's definition and use in context.

Sentiment Analysis

Measures the sentiment of phrases or chunks of text.
Applications: brand monitoring, crises management, customer feedback analysis, market research, targeted marketing campaigns, and influencer marketing.
Approaches:
- Rule-based algorithm composed by a human.
- Machine learning model learned from data.
- Valence Aware Dictionary for sEntiment Reasoning (VADER).

Advanced Approaches

Apply neural networks, e.g., decoder models like GPT.

Clustering Documents

Needs a measure for document similarity.
Measures:
- Jaccard Distance.
- Edit distance.
- Term Frequency-Inverse Document Frequency (TF-IDF) extension of the bag of words model.### TF-IDF and Word Embeddings
TF-IDF enhances the Bag of Words (BoW) model by weighing terms based on their importance, reducing the impact of frequently occurring but less informative words.
Word embeddings are dense vector representations of words that capture semantic meanings and relationships.
Unlike BoW, which represents words as sparse vectors with high dimensionality, word embeddings map words into a continuous vector space of lower dimensionality.

Word Embeddings

Word embeddings allow similar words to have similar vector representations, enabling the model to capture semantic relationships.
Words with similar meanings are close to each other in the vector space (e.g., "king" and "queen" are closer to each other than to "dog").
Word embeddings typically have lower dimensionality (e.g., 100 to 300 dimensions) compared to the size of the vocabulary in BoW.

Contextual Information

Embeddings can capture context if trained on large corpora, allowing them to understand the meaning of words in context.

Word Embedding Models

GloVe captures word relationships using a co-occurrence matrix, which counts how often words appear together in a corpus.
FastText extends Word2Vec by representing words as bags of character n-grams, allowing it to handle out-of-vocabulary words better.
Contextual Embeddings (e.g., BERT, ELMo) generate word embeddings that depend on the context in which the words appear, providing dynamic representations for words in different contexts.

Clustering and Topic Modeling

Standard ML clustering: K-means (partitive), Hierarchical (agglomerative)
Topic modeling: Latent Dirichlet Allocation (LDA) is a generative probabilistic model used for topic modeling.
LDA assumes that documents are mixtures of topics, and topics are mixtures of words.

Latent Dirichlet Allocation (LDA)

The goal of LDA is to discover the hidden topic structure in a collection of documents.
Each topic is characterized by a distribution over a fixed vocabulary of words.
Document Representation: Each document is represented as a distribution over topics.

Distributed Databases

Distributed databases offer scalability at a trade-off.
NoSQL databases offer flexibility in design and scalability.
CAP Theorem: Consistency, Availability, and Partition Tolerance.

Big Data and Analytics

Big data leaves us with two big questions: how to store big data and how to process big data.
Distributed computing is the key to processing big data.
Apache Spark is a unified engine for big data processing.

Apache Spark

Spark is a fast and general engine for large-scale data processing.
Resilient Distributed Datasets (RDD) are Spark's primary data abstraction.
DataFrames are like distributed in-memory tables with named columns and schemas.

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Description

This quiz covers text mining, natural language processing, sentiment analysis, customer segmentation, and more in the context of marketing. It explores the application of big data in marketing analytics and optimization.