This article introduces the Naive Bayes classifier, a probabilistic algorithm widely used for text classification tasks, particularly when efficiency and simplicity are important. As part of a broader exploration of algorithms for emotion classification from text, this post outlines the theoretical foundations of Naive Bayes and the planned steps for applying it as a baseline model.

Introduction to Naive Bayes

Naive Bayes is a family of probabilistic classifiers based on Bayes' Theorem, with the “naive” assumption that features are conditionally independent given the class.

In text classification tasks such as emotion detection, Naive Bayes can be used to estimate the probability that a piece of text belongs to a particular emotion category (e.g., joy, anger, sadness, etc.).

$$$ P(C_k \mid x) = \frac{P(x \mid C_k) \cdot P(C_k)}{P(x)} $$$

Where:

Ck is the class (e.g., an emotion label),
x is the input feature vector (e.g., a sentence),
P(Ck∣x) is the posterior probability of class CkC_kCk given input xxx,
P(x∣Ck) is the likelihood,
P(Ck) is the prior probability of class CkC_kCk,
P(x) is the evidence, a normalizing constant.

The predicted class is the one that maximizes the posterior:

$$\hat{C} = \arg\max_{C_k} P(C_k) \prod_{i=1}^{n} P(x_i \mid C_k)$$

Why It Works for Text

In text data, the features are usually the presence or frequency of words (after vectorization). The most commonly used version in NLP is the Multinomial Naive Bayes classifier, which models word occurrences and is well-suited for discrete features like term frequency or TF-IDF.

Despite the strong independence assumption, it often performs surprisingly well in real-world applications.

Planned Use in Emotion Classification

The goal is to apply Multinomial Naive Bayes as a baseline for classifying emotional tone in short text inputs. This approach will help evaluate how well a simple probabilistic model can distinguish between emotional categories like joy, fear, anger, and sadness.

Planned Implementation Steps

The following steps are scheduled for applying the algorithm:

Step 1: Data Preparation

Load a labeled emotion dataset (text + emotion labels)
Analyze class distribution and clean the data

Step 2: Feature Extraction

Preprocess text (lowercase, remove noise)
Apply TF-IDF vectorization to convert text into numerical features

Step 3: Model Training

Use MultinomialNB from scikit-learn
Perform an 80/20 train-test split
Fit the model to the training data

Step 4: Evaluation

Compute metrics:
- Accuracy
- Precision, Recall, F1-score per class
Analyze which emotions are easiest/hardest to predict

Tools and Libraries

The following environment and tools will be used:

Python 3.x
Google Colab or Jupyter Notebook
scikit-learn, pandas, numpy, matplotlib

Conclusion

Naive Bayes provides a strong starting point for classification problems involving text, offering speed, interpretability, and solid performance in many scenarios. Although it assumes independence among features — which is rarely true in language — its simplicity often outweighs this limitation in practice.

Naive Bayes for Emotion Classification in Text