Enhanced Sentiment Analysis Framework

Sentiment classification is a natural language processing task focused on identifying the emotional polarity (positive, negative, or neutral) expressed in textual content. A widely adopted baseline approach utilizes the Naive Bayes classifier, which computes sentiment probabilities based on word frequency distributions within the text corpus. The standard implementation involves: - Building a word frequency dictionary for each sentiment category - Applying Laplace smoothing to handle unseen words - Calculating prior probabilities from training data distributions To enhance this model, we can integrate contextual features and stylistic elements through: 1. N-gram integration: Implementing bigram/trigram features to capture phrase-level sentiment cues 2. Contextual weighting: Developing position-aware scoring where words in title/opening sentences receive higher weights 3. Style analysis: Incorporating linguistic features such as sentence length, punctuation frequency, and emotion lexicon matches The enhanced algorithm can be structured as: def enhanced_naive_bayes(text, model_params): # Preprocessing with stemming/lemmatization tokens = preprocess_text(text) # Calculate base Naive Bayes probability base_prob = calculate_naive_bayes_probability(tokens, model_params) # Apply contextual modifiers context_score = analyze_contextual_features(text, model_params) # Combine scores with weighted interpolation final_score = combine_scores(base_prob, context_score) return final_score This hybrid approach significantly improves classification accuracy by capturing both lexical patterns and meta-features, enabling more nuanced sentiment interpretation across diverse writing styles and domains.