You're reading from Data Labeling in Machine Learning with Python

Product typeBook

Published inJan 2024

PublisherPackt

ISBN-139781804610541

Edition1st Edition

Concepts

Machine Learning

Author (1)

Vijaya Kumar Suda

Labeling Text Data

In this chapter, we will explore techniques for labeling text data for classification in cases where an insufficient amount of labeled data is available. We are going to use Generative AI to label the text data, in addition to Snorkel and k-means clustering. The chapter focuses on the essential process of annotating textual data for NLP and text analysis. It aims to provide readers with practical knowledge and insights into various labeling techniques. The chapter will specifically cover automatic labeling using OpenAI, rule-based labeling using Snorkel labeling functions, and unsupervised learning using k-means clustering. By understanding these techniques, readers will be equipped to effectively label text data and extract meaningful insights from unstructured textual information.

We will cover the following sections in this chapter:

Real-world applications of text data labeling
Tools and frameworks for text data labeling
Exploratory data analysis...

Technical requirements

The code files used in this chapter are located at https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python/tree/main/code/Ch07.

The Gutenberg Corpus and movie review dataset can be found here:

You also need to create an Azure account and add the OpenAI resource for working with Generative AI. To sign up for a free Azure subscription, visit https://azure.microsoft.com/free. To request access to the Azure OpenAI service, visit https://aka.ms/oaiapply.

Once you have provisioned the Azure OpenAI service, set up the following environment variables:

os.environ['AZURE_OPENAI_KEY'] = 'your_api_key'
os.environ['AZURE_OPENAI_ENDPOINT") ='your_azure_openai_endpoint'

Your endpoint should look like https://YOUR_RESOURCE_NAME.openai.azure.com...

Real-world applications of text data labeling

Text data labeling or classification is widely used across various industries and applications to extract valuable information, automate processes, and improve decision-making. Here are some real-world examples across different use cases:

Customer support ticket classification:
- Use case: Companies receive a large volume of customer support tickets.
- Application: Automated classification of support tickets into categories such as Billing, Technical Support, and Product Inquiry. This helps prioritize and route tickets to the right teams.
Spam email filtering:
- Use case: Sorting emails into spam and non-spam categories.
- Application: Email providers use text classification to identify and filter out unwanted emails, providing users with a cleaner inbox and reducing the risk of phishing attacks.
Sentiment analysis in social media:
- Use case: Analyzing social media comments and posts.
- Application: Brands use sentiment analysis to gauge...

Tools and frameworks for text data labeling

There are several open source tools and frameworks available for text data analysis and labeling. Here are some popular ones, along with their pros and cons:

Exploratory data analysis of text

Exploratory Data Analysis (EDA) is a crucial step in any data science project. When it comes to text data, EDA can help us understand the structure and characteristics of the data, identify potential issues or inconsistencies, and inform our choice of data preprocessing and modeling techniques. In this section, we will walk through the steps involved in performing EDA on text data.

Loading the data

The first step in EDA is to load the text data into our environment. Text data can come in many formats, including plain text files, CSV files, or database tables. Once we have the data loaded, we can begin to explore its structure and content.

Understanding the data

The next step in EDA is to gain an understanding of the data. For text data, this may involve examining the size of the dataset, the number of documents or samples, and the overall structure of the text (e.g., whether it is structured or unstructured). We can use descriptive statistics...

Exploring Generative AI and OpenAI for labeling text data

Generative AI refers to a category of artificial intelligence that involves training models to generate new content or data based on patterns and information present in the training data. OpenAI is a prominent organization that has developed and released powerful generative models for various NLP tasks. One of the notable models is GPT, such as GPT-3, GPT-3.5, and GPT-4. These models have been influential in the fields of text data labeling and classification.

Generative AI focuses on training models to generate new data instances that resemble existing examples. It is often used for tasks such as text generation, image synthesis, and more. Generative models are trained on large datasets to learn underlying patterns, allowing them to generate coherent and contextually relevant content. In text-related tasks, generative AI can be applied to text completion, summarization, question answering, and even creative writing. Let...

Hands-on labeling of text data using the Snorkel API

In this section, we are going to learn how to label text data using the Snorkel API.

Snorkel provides an API for programmatically labeling text data using a small set of ground truth labels that are created by domain experts. Snorkel, an open source data labeling and training platform, is used by various companies and organizations across different industries, such as Google, Apple, Facebook, IBM, and SAP.

It has unique features that differentiate it from other competitors, especially in the context of weak supervision and programmatically generating labeled data. Here’s a comparison with some of the other tools:

Weak supervision: Snorkel excels in scenarios where labeled data is scarce, and manual labeling is expensive. It allows users to programmatically label large amounts of data using heuristics, patterns, and external resources.
Flexible labeling functions: Snorkel enables the creation of labeling functions...

Hands-on text labeling using Logistic Regression

Text labeling is a crucial task in NLP, enabling the categorization of textual data into predefined classes or sentiments. Logistic Regression, a popular machine learning algorithm, proves effective in text classification scenarios. In the following code, we walk through the process of using Logistic Regression to classify movie reviews into positive or negative sentiments. Here’s a breakdown of the code.

Step 1. Import necessary libraries and modules.

The code begins by importing the necessary libraries and modules. These include NLTK for NLP, scikit-learn for machine learning, and specific modules for sentiment analysis, text preprocessing, and classification:

    from nltk.corpus import stopwords
    from nltk.stem import WordNetLemmatizer
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.model_selection import...

Hands-on label prediction using K-means clustering

K-means clustering is a powerful unsupervised machine learning technique used for grouping similar data points into clusters. In the context of text data, K-means clustering can be employed to predict labels or categories for the given text based on their similarity. The provided code showcases how to utilize K-Means clustering to predict labels for movie reviews, breaking down the process into several key steps.

Step 1: Importing libraries and downloading data.

The following code begins by importing essential libraries such as scikit-learn and NLTK. It then downloads the necessary NLTK data, including the movie reviews dataset:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from nltk.corpus import movie_reviews
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import nltk
import re
# Download the necessary NLTK data
nltk.download('movie_reviews&apos...

Generating labels for customer reviews (sentiment analysis)

Customer reviews are a goldmine of information for businesses. Analyzing sentiment in customer reviews helps in understanding customer satisfaction, identifying areas for improvement, and making data-driven business decisions.

In the following example, we delve into sentiment analysis using a neural network model. The code utilizes TensorFlow and Keras to create a simple neural network architecture with an embedding layer, a flatten layer, and a dense layer. The model is trained on a small labeled dataset for sentiment classification, distinguishing between positive and negative sentiments. Following training, the model is employed to classify new sentences. The provided Python code demonstrates each step, from tokenizing and padding sequences to compiling, training, and making predictions.

The following dataset is used for training on sentiment analysis:

sentences = ["I love this movie", "This movie...

Summary

In this chapter, we delved into the realm of text data exploration using Python, gaining a comprehensive understanding of harnessing Generative AI and OpenAI models for effective text data labeling. Through code examples, we explored diverse text data labeling tasks, including classification, summarization, and sentiment analysis.

We then extended our knowledge by exploring Snorkel labeling functions, allowing us to label text data with enhanced flexibility. Additionally, we delved into the application of K-means clustering for labeling text data and concluded by discovering how to label customer reviews using neural networks.

With these acquired skills, you now possess the tools to unlock the full potential of your text data, extracting valuable insights for various applications. The next chapter awaits, where we will shift our focus to video data exploration, exploring different methods to gain insights from this dynamic data type.

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Author (1)

Vijaya Kumar Suda

Vijaya Kumar Suda is a seasoned data and AI professional boasting over two decades of expertise collaborating with global clients. Having resided and worked in diverse locations such as Switzerland, Belgium, Mexico, Bahrain, India, Canada, and the USA, Vijaya has successfully assisted customers spanning various industries. Currently serving as a senior data and AI consultant at Microsoft, he is instrumental in guiding industry partners through their digital transformation endeavors using cutting-edge cloud technologies and AI capabilities. His proficiency encompasses architecture, data engineering, machine learning, generative AI, and cloud solutions.
Read more about Vijaya Kumar Suda

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Tools and frameworks

Pros

Cons

Natural Language Toolkit (NLTK)

Comprehensive library for NLP tasks.

Rich set of tools for tokenization, stemming, tagging, parsing, and more.

Active community support.

Suitable for educational purposes and research projects.

Some components may not be as efficient for large-scale industrial applications.

Steep learning curve for beginners.

spaCy

Fast and efficient, designed for production use.

Pre-trained models for various languages.

Provides robust support for tokenization, named entity...