Feature Engineering in Sentiment Analysis

By Bill Sharlow

Day 4: Training a Sentiment Analysis Model

Welcome back to our sentiment analysis project! After exploring our dataset in the previous blog post, it’s time to roll up our sleeves and dive into feature engineering. Feature engineering plays a crucial role in machine learning projects, as it involves selecting, creating, and transforming features to improve model performance. In today’s post, we’ll explore different features that can be used for sentiment analysis and implement feature extraction techniques using Python.

Features for Sentiment Analysis

Before we delve into feature engineering, let’s discuss the types of features commonly used in sentiment analysis:

  1. Bag-of-Words (BoW): Represents text data as a bag of individual words, ignoring grammar and word order. Each document is represented by a vector where each dimension corresponds to a unique word in the vocabulary.
  2. Term Frequency-Inverse Document Frequency (TF-IDF): Similar to BoW, but weights each word by its frequency in the document and rarity across the entire dataset. This helps to prioritize important words while downplaying common ones.
  3. Word Embeddings: Represents words as dense vectors in a continuous vector space, where similar words are closer to each other. Word embeddings capture semantic relationships between words and are typically learned from large text corpora using techniques like Word2Vec, GloVe, or fastText.
  4. N-grams: Represents sequences of adjacent words instead of individual words. N-grams capture local word dependencies and can improve the model’s ability to capture context.

Implementing Feature Extraction

Now, let’s implement feature extraction techniques for our sentiment analysis project using Python and the scikit-learn library. We’ll start with bag-of-words (BoW) and TF-IDF representations:

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

# Bag-of-Words (BoW) representation
count_vectorizer = CountVectorizer(max_features=1000)  # Limit to top 1000 frequent words
X_bow = count_vectorizer.fit_transform(df['review'])

# TF-IDF representation
tfidf_vectorizer = TfidfVectorizer(max_features=1000)  # Limit to top 1000 features
X_tfidf = tfidf_vectorizer.fit_transform(df['review'])

# Display the shape of the feature matrices
print("Bag-of-Words (BoW) feature matrix shape:", X_bow.shape)
print("TF-IDF feature matrix shape:", X_tfidf.shape)


In this blog post, we’ve explored feature engineering techniques for sentiment analysis, including bag-of-words (BoW) and TF-IDF representations. These techniques help us transform text data into numerical features that can be used as input to machine learning models.

Stay tuned for tomorrow’s post, where we’ll discuss different machine learning models suitable for sentiment analysis and how to train them using our feature matrices.

If you have any questions or thoughts, feel free to share them in the comments section below!

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