Enhanced Sentiment Analysis with Improved Naive Bayes Implementation

Sentiment Analysis refers to the computational process of identifying and extracting subjective emotional information from text, representing one of the core applications in natural language processing. This article focuses on an enhanced Naive Bayes classification approach that demonstrates significant performance improvements over traditional implementations.

Traditional Naive Bayes operates under the assumption of feature independence, which offers computational efficiency but struggles to capture contextual relationships within text. The enhancement strategies typically include the following approaches:

First, implementing word embedding techniques (such as Word2Vec or GloVe) to replace simple word frequency counting, mapping discrete words into continuous vector spaces while preserving semantic relationships. In code implementation, this involves using pre-trained embedding layers or training custom embeddings on domain-specific corpora.

Second, employing bi-gram or tri-gram (n-gram) features to expand the feature dimension, effectively addressing misclassification issues with negated phrases like "not good." Programmatically, this requires feature engineering pipelines that generate n-gram combinations and calculate their respective probabilities.

Finally, applying Laplace smoothing to handle low-frequency word interference and integrating chi-square tests for feature selection to remove noisy vocabulary with low classification contribution. The code implementation typically involves sklearn's feature selection modules combined with custom smoothing parameters in the probability calculations.

The enhanced model demonstrates excellent performance in short-text scenarios such as e-commerce reviews and social media content, particularly when handling sarcasm and implicit expressions, where the F1-score shows 15%-20% improvement over the baseline version. During actual deployment, domain adaptation must be considered—sentiment expression dictionaries require targeted optimization across different industries (e.g., healthcare vs. entertainment), which can be implemented through domain-specific training data and custom vocabulary weighting.