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// Topic Identification Algorithm (Blogger-Optimized HTML)

FUNCTION determineTopic(textParagraph) {
    // Step 1: Preprocess the text
    cleanedText = convertToLowerCase(textParagraph);
    tokens = tokenize(cleanedText);
    filteredTokens = removeStopWords(tokens);
    stemmedWords = applyStemming(filteredTokens);

    // Step 2: Extract key concepts and entities
    keywordCounts = calculateTermFrequency(stemmedWords);
    namedEntities = extractNamedEntities(textParagraph);
    topKeywords = getTopNItems(keywordCounts, n = 5);

    // Step 3: Match against a knowledge base or domain taxonomy
    candidateTopics = queryKnowledgeGraph(topKeywords, namedEntities);
    bestMatch = null;
    highestScore = 0.0;

    FOR EACH topic IN candidateTopics DO
        relevanceScore = computeSemanticSimilarity(topic, topKeywords);
        IF relevanceScore > highestScore THEN
            highestScore = relevanceScore;
            bestMatch = topic;
        END IF
    END FOR

    // Step 4: Fallback to text summarization if no strong match
    IF highestScore < threshold THEN
        bestMatch = generateExtractiveSummary(textParagraph, maxLength = 3);
    END IF

    RETURN bestMatch;
}
END FUNCTION