geo/server/internal/tenant/app/monitoring_answer_parse.go

package app

import (
	"strings"
	"unicode"
)

type monitoringStoredParseFields struct {
	BrandMentioned       *bool
	BrandMentionPosition *string
	FirstRecommended     *bool
	SentimentLabel       *string
	MatchedBrandTerms    []string
}

type monitoringAnswerParseSummary struct {
	BrandMentioned       bool
	BrandMentionPosition string
	FirstRecommended     bool
	SentimentLabel       string
	MatchedBrandTerms    []string
}

var monitoringPositiveSignals = []string{
	"推荐", "首选", "优先", "更推荐", "值得", "靠谱", "专业", "优质", "高端", "不错", "合适", "满意", "口碑", "领先",
}

var monitoringNegativeSignals = []string{
	"不推荐", "慎选", "避坑", "不好", "较差", "很差", "问题", "投诉", "负面", "不靠谱", "一般", "短板", "缺点", "不足",
}

func resolveMonitoringAnswerParseSummary(
	answer string,
	brandName string,
	stored monitoringStoredParseFields,
) monitoringAnswerParseSummary {
	return overlayMonitoringStoredParseSummary(
		deriveMonitoringAnswerParseSummary(answer, brandName),
		stored,
	)
}

func overlayMonitoringStoredParseSummary(
	base monitoringAnswerParseSummary,
	stored monitoringStoredParseFields,
) monitoringAnswerParseSummary {
	result := base

	if stored.BrandMentioned != nil {
		result.BrandMentioned = *stored.BrandMentioned
	}
	if value := strings.TrimSpace(monitoringStringValue(stored.BrandMentionPosition)); value != "" {
		result.BrandMentionPosition = value
	}
	if stored.FirstRecommended != nil {
		result.FirstRecommended = *stored.FirstRecommended
	}
	if value := strings.TrimSpace(monitoringStringValue(stored.SentimentLabel)); value != "" {
		result.SentimentLabel = value
	}
	if len(stored.MatchedBrandTerms) > 0 {
		result.MatchedBrandTerms = normalizeMonitoringBrandTermList(stored.MatchedBrandTerms)
	}

	if result.BrandMentioned && result.BrandMentionPosition == "" {
		result.BrandMentionPosition = "mentioned"
	}
	if !result.BrandMentioned && result.BrandMentionPosition == "" {
		result.BrandMentionPosition = "not_mentioned"
	}
	if result.BrandMentioned && result.SentimentLabel == "" {
		result.SentimentLabel = "neutral"
	}
	if !result.BrandMentioned && result.SentimentLabel == "" {
		result.SentimentLabel = "unknown"
	}

	return result
}

func deriveMonitoringAnswerParseSummary(answer string, brandName string) monitoringAnswerParseSummary {
	result := monitoringAnswerParseSummary{
		BrandMentionPosition: "not_mentioned",
		SentimentLabel:       "unknown",
		MatchedBrandTerms:    []string{},
	}

	answer = strings.TrimSpace(answer)
	if answer == "" {
		return result
	}

	brandTerms := buildMonitoringBrandTerms(brandName)
	if len(brandTerms) == 0 {
		return result
	}

	answerLower := strings.ToLower(answer)
	answerNormalized := normalizeMonitoringTextForMatch(answer)
	matchedTerms := findMonitoringMatchedBrandTerms(answerLower, answerNormalized, brandTerms)
	if len(matchedTerms) == 0 {
		return result
	}

	result.BrandMentioned = true
	result.BrandMentionPosition = "mentioned"
	result.MatchedBrandTerms = matchedTerms

	if isMonitoringTop1Mention(answerLower, matchedTerms) {
		result.BrandMentionPosition = "top1"
	}

	result.FirstRecommended = isMonitoringFirstRecommended(answerLower, matchedTerms, result.BrandMentionPosition)
	result.SentimentLabel = deriveMonitoringSentimentLabel(answerLower, matchedTerms, result.FirstRecommended)
	return result
}

func buildMonitoringBrandTerms(brandName string) []string {
	return normalizeMonitoringBrandTermList([]string{strings.TrimSpace(brandName)})
}

func normalizeMonitoringBrandTermList(values []string) []string {
	result := make([]string, 0, len(values))
	seen := make(map[string]struct{}, len(values))
	for _, value := range values {
		trimmed := strings.TrimSpace(value)
		if trimmed == "" {
			continue
		}
		if _, ok := seen[trimmed]; ok {
			continue
		}
		seen[trimmed] = struct{}{}
		result = append(result, trimmed)
	}
	return result
}

func normalizeMonitoringTextForMatch(value string) string {
	var builder strings.Builder
	builder.Grow(len(value))
	for _, item := range strings.ToLower(value) {
		if unicode.IsLetter(item) || unicode.IsNumber(item) {
			builder.WriteRune(item)
		}
	}
	return builder.String()
}

func findMonitoringMatchedBrandTerms(answerLower, answerNormalized string, brandTerms []string) []string {
	matched := make([]string, 0, len(brandTerms))
	for _, term := range brandTerms {
		termLower := strings.ToLower(strings.TrimSpace(term))
		if termLower == "" {
			continue
		}
		termNormalized := normalizeMonitoringTextForMatch(termLower)
		if strings.Contains(answerLower, termLower) || (termNormalized != "" && strings.Contains(answerNormalized, termNormalized)) {
			matched = append(matched, term)
		}
	}
	return normalizeMonitoringBrandTermList(matched)
}

func isMonitoringTop1Mention(answerLower string, matchedTerms []string) bool {
	segment := strings.ToLower(firstMonitoringRunes(strings.TrimSpace(answerLower), 96))
	if segment == "" {
		return false
	}
	for _, term := range matchedTerms {
		termLower := strings.ToLower(strings.TrimSpace(term))
		if termLower != "" && strings.Contains(segment, termLower) {
			return true
		}
	}
	return false
}

func isMonitoringFirstRecommended(answerLower string, matchedTerms []string, mentionPosition string) bool {
	if mentionPosition == "top1" {
		return true
	}

	for _, term := range matchedTerms {
		termLower := strings.ToLower(strings.TrimSpace(term))
		if termLower == "" {
			continue
		}

		patterns := []string{
			"推荐" + termLower,
			"首选" + termLower,
			"优先" + termLower,
			"建议选择" + termLower,
			"建议选" + termLower,
			"更推荐" + termLower,
			termLower + "是首选",
			termLower + "更合适",
			termLower + "更值得",
			termLower + "值得推荐",
		}
		for _, pattern := range patterns {
			if strings.Contains(answerLower, pattern) {
				return true
			}
		}
	}
	return false
}

func deriveMonitoringSentimentLabel(answerLower string, matchedTerms []string, firstRecommended bool) string {
	negativeCount := 0
	for _, signal := range monitoringNegativeSignals {
		if strings.Contains(answerLower, signal) {
			negativeCount++
		}
	}
	if negativeCount > 0 {
		return "negative"
	}

	positiveCount := 0
	for _, signal := range monitoringPositiveSignals {
		if strings.Contains(answerLower, signal) {
			positiveCount++
		}
	}
	if firstRecommended || positiveCount > 0 {
		return "positive"
	}
	if len(matchedTerms) > 0 {
		return "neutral"
	}
	return "unknown"
}

func firstMonitoringRunes(value string, limit int) string {
	if limit <= 0 {
		return ""
	}
	runes := []rune(value)
	if len(runes) <= limit {
		return string(runes)
	}
	return string(runes[:limit])
}