tercul-backend/linguistics/text_analyzer.go

package linguistics

import (
	"context"
	"sync"
)

// TextAnalyzer defines the interface for pure text analysis operations
type TextAnalyzer interface {
	// AnalyzeText performs linguistic analysis on the given text
	AnalyzeText(ctx context.Context, text string, language string) (*AnalysisResult, error)

	// AnalyzeTextConcurrently performs text analysis using concurrent processing
	AnalyzeTextConcurrently(ctx context.Context, text string, language string, concurrency int) (*AnalysisResult, error)
}

// BasicTextAnalyzer implements the TextAnalyzer interface with simple algorithms
type BasicTextAnalyzer struct {
	langDetector      LanguageDetector
	sentimentProvider SentimentProvider
	keywordProvider   KeywordProvider
}

// NewBasicTextAnalyzer creates a new BasicTextAnalyzer
func NewBasicTextAnalyzer() *BasicTextAnalyzer {
	return &BasicTextAnalyzer{}
}

// WithLanguageDetector injects a language detector provider
func (a *BasicTextAnalyzer) WithLanguageDetector(detector LanguageDetector) *BasicTextAnalyzer {
	a.langDetector = detector
	return a
}

// WithSentimentProvider injects a sentiment provider
func (a *BasicTextAnalyzer) WithSentimentProvider(provider SentimentProvider) *BasicTextAnalyzer {
	a.sentimentProvider = provider
	return a
}

// WithKeywordProvider injects a keyword provider
func (a *BasicTextAnalyzer) WithKeywordProvider(provider KeywordProvider) *BasicTextAnalyzer {
	a.keywordProvider = provider
	return a
}

// AnalyzeText performs linguistic analysis on the given text
func (a *BasicTextAnalyzer) AnalyzeText(ctx context.Context, text string, language string) (*AnalysisResult, error) {
	if text == "" {
		return &AnalysisResult{}, nil
	}

	// Auto-detect language if not provided and a detector exists
	if language == "" && a.langDetector != nil {
		if detected, ok := a.langDetector.DetectLanguage(text); ok {
			language = detected
		}
	}

	result := &AnalysisResult{
		PartOfSpeechCounts: make(map[string]int),
		Entities:           []Entity{},
		Keywords:           []Keyword{},
		Topics:             []Topic{},
	}

	// Perform a single pass through the text for basic statistics
	words, sentences, paragraphs, avgWordLength := analyzeTextBasicStats(text)

	result.WordCount = words
	result.SentenceCount = sentences
	result.ParagraphCount = paragraphs
	result.AvgWordLength = avgWordLength

	// Calculate sentence length average
	if result.SentenceCount > 0 {
		result.AvgSentenceLength = float64(result.WordCount) / float64(result.SentenceCount)
	}

	// Calculate readability score (simplified Flesch-Kincaid)
	result.ReadabilityScore = calculateReadabilityScore(result.AvgSentenceLength, result.AvgWordLength)
	result.ReadabilityMethod = "Simplified Flesch-Kincaid"

	// Extract keywords: prefer provider if available
	if a.keywordProvider != nil {
		if kws, err := a.keywordProvider.Extract(text, language); err == nil {
			result.Keywords = kws
		} else {
			result.Keywords = extractKeywordsOptimized(text, language)
		}
	} else {
		result.Keywords = extractKeywordsOptimized(text, language)
	}

	// Sentiment: prefer provider if available
	if a.sentimentProvider != nil {
		if score, err := a.sentimentProvider.Score(text, language); err == nil {
			result.Sentiment = score
		} else {
			result.Sentiment = estimateSentimentOptimized(text, language)
		}
	} else {
		result.Sentiment = estimateSentimentOptimized(text, language)
	}

	return result, nil
}

// AnalyzeTextConcurrently performs text analysis using concurrent processing
func (a *BasicTextAnalyzer) AnalyzeTextConcurrently(ctx context.Context, text string, language string, concurrency int) (*AnalysisResult, error) {
	if text == "" {
		return &AnalysisResult{}, nil
	}

	// Auto-detect language if not provided and a detector exists
	if language == "" && a.langDetector != nil {
		if detected, ok := a.langDetector.DetectLanguage(text); ok {
			language = detected
		}
	}

	// Split the text into chunks for concurrent processing
	chunks := splitTextIntoChunks(text, concurrency)
	n := len(chunks)

	// Create channels for results sized by actual chunks to avoid deadlocks
	wordCountCh := make(chan int, n)
	sentenceCountCh := make(chan int, n)
	paragraphCountCh := make(chan int, n)
	wordLengthSumCh := make(chan float64, n)
	wordLengthCountCh := make(chan int, n)
	keywordsCh := make(chan []Keyword, n)
	sentimentCh := make(chan float64, n)

	// Process each chunk concurrently
	var wg sync.WaitGroup
	for _, chunk := range chunks {
		wg.Add(1)
		go func(chunkText string) {
			defer wg.Done()
			select {
			case <-ctx.Done():
				return
			default:
			}

			// Basic statistics
			words, sentences, paragraphs, wordLengthSum, wordCount := analyzeChunkBasicStats(chunkText)
			wordCountCh <- words
			sentenceCountCh <- sentences
			paragraphCountCh <- paragraphs
			wordLengthSumCh <- wordLengthSum
			wordLengthCountCh <- wordCount

			// Keywords (provider if available)
			if a.keywordProvider != nil {
				if kws, err := a.keywordProvider.Extract(chunkText, language); err == nil {
					keywordsCh <- kws
				} else {
					keywordsCh <- extractKeywordsOptimized(chunkText, language)
				}
			} else {
				keywordsCh <- extractKeywordsOptimized(chunkText, language)
			}

			// Sentiment (provider if available)
			if a.sentimentProvider != nil {
				if score, err := a.sentimentProvider.Score(chunkText, language); err == nil {
					sentimentCh <- score
				} else {
					sentimentCh <- estimateSentimentOptimized(chunkText, language)
				}
			} else {
				sentimentCh <- estimateSentimentOptimized(chunkText, language)
			}
		}(chunk)
	}

	// Wait for all goroutines to complete
	wg.Wait()
	close(wordCountCh)
	close(sentenceCountCh)
	close(paragraphCountCh)
	close(wordLengthSumCh)
	close(wordLengthCountCh)
	close(keywordsCh)
	close(sentimentCh)

	// Aggregate results
	result := &AnalysisResult{
		PartOfSpeechCounts: make(map[string]int),
		Entities:           []Entity{},
		Keywords:           []Keyword{},
		Topics:             []Topic{},
	}

	// Sum up basic statistics
	for wc := range wordCountCh {
		result.WordCount += wc
	}
	for sc := range sentenceCountCh {
		result.SentenceCount += sc
	}
	for pc := range paragraphCountCh {
		result.ParagraphCount += pc
	}

	// Calculate average word length
	var totalWordLengthSum float64
	var totalWordCount int
	for wls := range wordLengthSumCh {
		totalWordLengthSum += wls
	}
	for wlc := range wordLengthCountCh {
		totalWordCount += wlc
	}
	if totalWordCount > 0 {
		result.AvgWordLength = totalWordLengthSum / float64(totalWordCount)
	}

	// Calculate sentence length average
	if result.SentenceCount > 0 {
		result.AvgSentenceLength = float64(result.WordCount) / float64(result.SentenceCount)
	}

	// Calculate readability score
	result.ReadabilityScore = calculateReadabilityScore(result.AvgSentenceLength, result.AvgWordLength)
	result.ReadabilityMethod = "Simplified Flesch-Kincaid"

	// Merge keywords
	keywordSum := make(map[string]float64)
	keywordCount := make(map[string]int)
	for kws := range keywordsCh {
		for _, kw := range kws {
			keywordSum[kw.Text] += kw.Relevance
			keywordCount[kw.Text]++
		}
	}
	for text, sum := range keywordSum {
		cnt := keywordCount[text]
		if cnt > 0 {
			result.Keywords = append(result.Keywords, Keyword{Text: text, Relevance: sum / float64(cnt)})
		}
	}

	// Average sentiment
	var totalSentiment float64
	var sentimentCount int
	for s := range sentimentCh {
		totalSentiment += s
		sentimentCount++
	}
	if sentimentCount > 0 {
		result.Sentiment = totalSentiment / float64(sentimentCount)
	}

	return result, nil
}