Smart Subject Email Line Generation with Word2Vec

Introduction

Imagine you’re tasked with crafting the perfect subject line for a crucial email campaign, but standing out in a crowded inbox seems daunting. This article offers a solution with a step-by-step guide to Smart Subject Email Line Generation with Word2Vec. Discover how to harness the power of Word2Vec embeddings to create compelling and contextually relevant subject lines that captivate and engage your audience. Follow along to transform your approach and elevate your email marketing strategy.

Learning Objectives

• Learn what vector embeddings are and how they represent complex data as numerical vectors.
• Learn how to compute semantic similarity between different pieces of text using cosine similarity.
• Build a system that can generate contextually relevant email subject lines using Word2Vec and NLTK.

This article was published as a part of the Data Science Blogathon.

Embedding Models: Transforming Words into Numerical Vectors

Word embeddings is a method which is used to represent words efficiently in a dense numerical format, where similar words have similar encodings. Unlike manually setting these encodings, embeddings are trainable parameters—floating point values learned by the model during training, similar to how weights are learned in a dense layer. Embeddings range from 8 for smaller datasets to larger dimensions like 1024 for extensive datasets allowing them to capture relationships between words. This higher dimensionality enables embeddings to encode detailed semantic relationships.

In a word embedding diagram, a 4-dimensional vector of floating-point values represents each word. Think of embeddings as a “lookup table” that stores each word’s dense vector after training, allowing you to quickly encode and retrieve words based on their vector representations.

Defining Semantic Similarity and Its Significance

Semantic similarity is the measure of how closely two pieces of text convey the same meaning. It allows systems to understand the different ways ideas can be expressed in language without needing to explicitly define each variation.

Introduction to Word2Vec and Its Functionalities

Word2Vec is a popular natural language processing technique for converting words into numerical vector representations.

Word2Vec generates word embedding which are continuous vector representations of words. Unlike traditional one hot encoding which represents words as sparse vectors Word2Vec maps each word to a dense vector of fixed size. These vectors capture semantic relationships between words allowing similar words to have similar vectors.

Training Methods of Word2Vec

Word2Vec employs two main training approaches:

Continuous Bag of Words

This method predicts a target word based on its surrounding context words. For example if a word is missing from a sentence CBOW tries to infer the missing word using the context provided by the other words in the sentence.

Skip-Gram

 During training Word2Vec refines the word vectors by analyzing how frequently words appear together within a defined context window. Words with more comparable vectors are those that appear in similar contexts. Relationships like synonyms and analogies are well captured by this method (for example, the relationship between “king” and “queen” can be deduced from the analogy “king” – “man” +  “queen” –  “woman”).

Working Mechanism of Word2Vec

• Initialization: Start with random vectors for each word in the vocabulary.
• Training: For each word in a given context, update the vectors to minimize the prediction error between the actual and predicted words. This involves backpropagation and optimization techniques such as stochastic gradient descent.
• Vector Representation: After training, each word is represented by a vector that encodes its semantic meaning. Words with similar meanings or contexts will have vectors that are close to each other in the vector space.

Read more about Word2Vec here

Step-by-Step Guide to Smart Email Subject Line Generation

Unlock the secrets to crafting compelling email subject lines with our step-by-step guide, leveraging Word2Vec embeddings for smarter, more relevant results.

Step1: Setting Up the Environment and Preprocessing Data

Import essential libraries for data manipulation, natural language processing, word embeddings, and similarity calculations.

import pandas as pd
import nltk
from nltk.tokenize import word_tokenize
from gensim.models import Word2Vec
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

Step2: Download NLTK Data

Download the NLTK tokenizer data required for tokenizing text.

# Download NLTK data (only needed once)
nltk.download('punkt')

Step3: Read the CSV File

Load the email dataset from a CSV file and handle any potential parsing errors.

# Read the CSV file
try:
    df = pd.read_csv('emails.csv', quotechar='"', escapechar='\\', engine='python', on_bad_lines='skip')
except pd.errors.ParserError as e:
    print(f"Error reading the CSV file: {e}")

Step4: Tokenize Email Bodies

Tokenize the email bodies into words and convert them to lowercase for uniformity.

# Preprocess: Tokenize email bodies
tokenized_bodies = [word_tokenize(body.lower()) for body in df['email_body']]

Step5: Train the Word2Vec Model

Train a Word2Vec model on the tokenized email bodies to create word embeddings.

# Train Word2Vec model on the email bodies
word2vec_model = Word2Vec(sentences=tokenized_bodies, vector_size=100, window=5, min_count=1, workers=4)

Step6: Define a Function to Compute Document Embeddings

Create a function that computes the embedding of an email body by averaging the embeddings of its words.

# Function to compute document embedding by averaging word embeddings
def get_document_embedding(doc, model):
    words = word_tokenize(doc.lower())
    word_embeddings = [model.wv[word] for word in words if word in model.wv]
    if word_embeddings:
        return np.mean(word_embeddings, axis=0)
    else:
        return np.zeros(model.vector_size)

Step7: Compute Embeddings for All Email Bodies

Calculate the document embeddings for all email bodies in the dataset.

# Compute embeddings for all email bodies
body_embeddings = np.array([get_document_embedding(body, word2vec_model) for body in df['email_body']])

Step8: Define a Function for Semantic Search

Create a function that finds the most similar email body in the dataset to a given query using cosine similarity.

# Function to perform semantic search based on the email body
def semantic_search(query, model, body_embeddings, texts):
    query_embedding = get_document_embedding(query, model)
    similarities = cosine_similarity([query_embedding], body_embeddings)
    best_match_idx = np.argmax(similarities)
    return texts[best_match_idx], similarities[0, best_match_idx]

Step9: Example Email Body for Subject Line Generation

Define a new email body for which to generate a subject line.

# Example email body for which to generate a subject line
new_email_body = "Please review the attached documents and provide feedback by end of day"

Step10: Perform Semantic Search for the New Email Body

Use the semantic search function to find the most similar email body in the dataset to the new email body.

# Perform semantic search for the new email body to find the most similar existing email
matched_text, similarity_score = semantic_search(new_email_body, word2vec_model, body_embeddings, df['email_body'])

Step11: Retrieve the Corresponding Subject Line

Retrieve and print the subject line corresponding to the matched email body, along with the matched email body and similarity score.

# Find the corresponding subject line for the matched email body
matched_subject = df.loc[df['email_body'] == matched_text, 'subject_line'].values[0]

print("Generated Subject Line:", matched_subject)
print("Matched Email Body:", matched_text)
print("Similarity Score:", similarity_score)

Step12: Evaluate Accuracy (Example)

Evaluating the accuracy of a model is crucial to understand its performance on unseen data. In this step, we will define the function evaluate_accuracy, use a test dataset (test_df), and precomputed embeddings (train_body_embeddings) to measure the accuracy of the model.

# Evaluate accuracy on the test set
accuracy = evaluate_accuracy(test_df, word2vec_model, train_body_embeddings, train_df['email_body'])
print("Mean Cosine Similarity for Test Set:", accuracy)

I have made use of Document dataset for code implementation which can be found here.

Output

A sneek-peak into the dataset :

Real Example

Let’s walk through a real example to illustrate this step.

Assume we have a test set (test_df) with the following email bodies and subject lines:

import pandas as pd
import nltk
from nltk.tokenize import word_tokenize
from gensim.models import Word2Vec
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

# Download NLTK data (only needed once)
nltk.download('punkt')

# Example training dataset
train_data = {
    'email_body': [
        "Please send me the latest sales report.",
        "Can you provide feedback on the attached document?",
        "Let's schedule a meeting to discuss the new project.",
        "Review the quarterly financials and get back to me."
    ],
    'subject_line': [
        "Request for Sales Report",
        "Feedback on Document",
        "Meeting for New Project",
        "Quarterly Financial Review"
    ]
}
train_df = pd.DataFrame(train_data)

# Example test dataset
test_data = {
    'email_body': [
        "Can you provide the latest sales figures?",
        "Please review the attached documents and provide feedback.",
        "Schedule a meeting to discuss the new project proposal."
    ],
    'subject_line': [
        "Request for Latest Sales Figures",
        "Feedback on Attached Documents",
        "Meeting for Project Proposal"
    ]
}
test_df = pd.DataFrame(test_data)

# Preprocess: Tokenize email bodies
tokenized_bodies = [word_tokenize(body.lower()) for body in train_df['email_body']]

# Train Word2Vec model on the email bodies
word2vec_model = Word2Vec(sentences=tokenized_bodies, vector_size=100, window=5, min_count=1, workers=4)

# Function to compute document embedding by averaging word embeddings
def get_document_embedding(doc, model):
    words = word_tokenize(doc.lower())
    word_embeddings = [model.wv[word] for word in words if word in model.wv]
    if word_embeddings:
        return np.mean(word_embeddings, axis=0)
    else:
        return np.zeros(model.vector_size)

# Compute embeddings for all email bodies in the training set
train_body_embeddings = np.array([get_document_embedding(body, word2vec_model) for body in train_df['email_body']])

# Function to evaluate the accuracy of the model on the test set
def evaluate_accuracy(test_df, model, train_body_embeddings, train_texts):
    similarities = []

    for index, row in test_df.iterrows():
        # Compute the embedding for the current email body in the test set
        test_embedding = get_document_embedding(row['email_body'], model)

        # Compute cosine similarities between the test embedding and all training email body embeddings
        cos_sim = cosine_similarity([test_embedding], train_body_embeddings)

        # Get the highest similarity score
        best_match_idx = np.argmax(cos_sim)
        highest_similarity = cos_sim[0, best_match_idx]

        similarities.append(highest_similarity)

    # Return the mean cosine similarity
    return np.mean(similarities)

# Evaluate accuracy on the test set
accuracy = evaluate_accuracy(test_df, word2vec_model, train_body_embeddings, train_df['email_body'])
print("Mean Cosine Similarity for Test Set:", accuracy)

Output:

Mean Cosine Similarity for Test Set: 0.86

Challenges

• Cleaning and preparing the email dataset for training can have issues like malformed rows or inconsistent formats.
• The model might struggle to generate relevant subject lines for completely new or unique email bodies that differ significantly from the training data

Conclusion 

The project shows how to generate smart email subject lines easier by using Word2Vec embeddings. To produce vector embeddings of email bodies the procedure consists of preprocessing the email data and training a Word2Vec model. Further enhancements include incorporating more sophisticated models and optimizing the methodology for enhanced efficacy. Applications for this concept can be for a company that wants to improve their open open rates of their email marketing campaigns by using more engaging and relevant subject lines. A news website wants to send personalized newsletters to its subscribers based on their reading preferences.

Key Takeaways

• Learn how Word2Vec transforms words into numerical vectors to represent semantic relationships.
• Discover how the quality of word embeddings directly impacts the relevance of generated topic lines.
• Recognizing how to match fresh email bodies with current ones using cosine similarity.

Frequently Asked Questions

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Aadya Singh

Aadya Singh is a passionate and enthusiastic individual excited about sharing her knowledge and growing alongside the vibrant Analytics Vidhya Community. Armed with a Bachelor's degree in Bio-technology from MS Ramaiah Institute of Technology in Bangalore, India, she embarked on a journey that would lead her into the intriguing realms of Machine Learning (ML) and Natural Language Processing (NLP).

Aadya's fascination with technology and its potential began with a profound curiosity about how computers can replicate human intelligence. This curiosity served as the catalyst for her exploration of the dynamic fields of ML and NLP, where she has since been captivated by the immense possibilities for creating intelligent systems.

With her academic background in bio-technology, Aadya brings a unique perspective to the world of data science and artificial intelligence. Her interdisciplinary approach allows her to blend her scientific knowledge with the intricacies of ML and NLP, creating innovative and impactful solutions.