MoodDetectionService，主要根據使用者輸入的文字，自動偵測並判斷心情。用於聊天室，讓系統能根據使用者的心情給予更貼近需求的回應或建議。

核心功能說明：

1. 關鍵字比對

   服務初始化時，依照已定義的心情相關的關鍵字，例如「開心」、「放鬆」、「疲憊」、「專注」等，以及英文對應詞（如 happy, relaxed, tired），將使用者的輸入分割成詞語，然後逐一比對每個 mood 的關鍵字，並根據關鍵字長度加分。系統會先將內容轉為小寫，並根據偵測到的關鍵字，判定對應的心情。若有多個心情被偵測，取分數最高者，並依分數計算信心度。

   private final Map<String, Set<String>> moodKeywords = initializeMoodKeywords();
   
   private String getMoodDescription(String mood) {
       switch (mood) {
           case "happy": return "開心愉快";
           case "relaxed": return "放鬆平靜";
           case "tired": return "疲憊想休息";
           case "energetic": return "充滿活力";
           case "stressed": return "有壓力焦慮";
           case "sad": return "傷心沮喪";
           case "romantic": return "浪漫溫馨";
           case "focused": return "專注工作";
           case "refreshed": return "需要清爽";
           case "cozy": return "溫暖舒適";
           default: return "中性心情";
       }
   }
   
   private Map<String, Set<String>> initializeMoodKeywords() {
       Map<String, Set<String>> keywords = new HashMap<>();
   
       // 開心/興奮
       keywords.put("happy", new HashSet<>(Arrays.asList(
           "開心", "高興", "快樂", "興奮", "激動", "愉快", "喜悅", "歡樂",
           "happy", "excited", "joyful", "cheerful", "delighted", "elated"
       )));
   
       // 放鬆/平靜
       keywords.put("relaxed", new HashSet<>(Arrays.asList(
           "放鬆", "平靜", "安靜", "冷靜", "舒服", "悠閒", "輕鬆", "寧靜",
           "relaxed", "calm", "peaceful", "tranquil", "serene", "zen"
       )));
   
       // 疲憊/累
       keywords.put("tired", new HashSet<>(Arrays.asList(
           "累", "疲憊", "疲勞", "困", "想睡", "無力", "倦", "乏",
           "tired", "exhausted", "weary", "sleepy", "drained", "fatigue"
       )));
   
       // 有活力/精神
       keywords.put("energetic", new HashSet<>(Arrays.asList(
           "有活力", "精神", "充滿活力", "有精神", "清醒", "振奮", "活躍",
           "energetic", "vigorous", "lively", "active", "alert", "refreshed"
       )));
   
       // 壓力/焦慮
       keywords.put("stressed", new HashSet<>(Arrays.asList(
           "壓力", "焦慮", "緊張", "煩躁", "不安", "憂心", "緊迫", "煩惱",
           "stressed", "anxious", "worried", "nervous", "tense", "overwhelmed"
       )));
   
       // 悲傷/沮喪
       keywords.put("sad", new HashSet<>(Arrays.asList(
           "傷心", "難過", "沮喪", "憂鬱", "低落", "失望", "鬱悶", "悲傷",
           "sad", "depressed", "down", "disappointed", "melancholy", "blue"
       )));
   
       // 浪漫/溫馨
       keywords.put("romantic", new HashSet<>(Arrays.asList(
           "浪漫", "甜蜜", "溫馨", "約會", "情人", "戀愛", "愛情",
           "romantic", "warm", "sweet", "intimate", "loving"
           // "cozy" 已移除，避免與 cozy mood 混淆
       )));
   
       // 專注/工作
       keywords.put("focused", new HashSet<>(Arrays.asList(
           "專注", "工作", "學習", "讀書", "思考", "集中", "努力", "認真",
           "focused", "concentrated", "studying", "working", "productive"
       )));
   
       // 清爽/提神
       keywords.put("refreshed", new HashSet<>(Arrays.asList(
           "清爽", "提神", "refresh", "refreshed", "醒腦", "涼快", "cool", "fresh"
       )));
   
       // 溫暖/舒適
       keywords.put("cozy", new HashSet<>(Arrays.asList(
           "溫暖", "舒適", "cozy", "暖和", "暖", "comfortable", "snug"
       )));
   
       return keywords;
   }
   
   public MoodDetectionResult detectMood(String input) {
       if (input == null || input.trim().isEmpty()) {
           return new MoodDetectionResult("neutral", "無特定心情", 0.0);
       }
   
       String lowerInput = input.toLowerCase();
       Map<String, Integer> moodScores = new HashMap<>();
   
       // 以空白、標點分割詞語
       String[] words = lowerInput.split("[\\s\\p{Punct}]+");
       Set<String> wordSet = new HashSet<>(Arrays.asList(words));
   
       // 計算每種心情的匹配分數（完整詞語比對）
       for (Map.Entry<String, Set<String>> entry : moodKeywords.entrySet()) {
           String mood = entry.getKey();
           Set<String> keywords = entry.getValue();
   
           int score = 0;
           for (String keyword : keywords) {
               String lowerKeyword = keyword.toLowerCase();
               // 完整詞語比對
               if (wordSet.contains(lowerKeyword)) {
                   score += lowerKeyword.length(); // 較長的關鍵字有更高的權重
               }
           }
   
           if (score > 0) {
               moodScores.put(mood, score);
           }
       }
   
       // 找出最高分的心情
       if (moodScores.isEmpty()) {
           return detectMoodFromContext(input);
       }
   
       String detectedMood = Collections.max(moodScores.entrySet(), 
                                           Map.Entry.comparingByValue()).getKey();
       int maxScore = moodScores.get(detectedMood);
       double confidence = Math.min(maxScore / 10.0, 1.0); // 信心度標準化為 0-1
   
     return new MoodDetectionResult(detectedMood, getMoodDescription(detectedMood), confidence);
   }
   

2. 情境推測

   除了直接比對的心情關鍵字，MoodDetectionService 也能透過分析輸入中的時間（早上、晚上）、天氣（炎熱、寒冷）等。這些情境詞彙有助於推測使用者可能的心情，例如「寒冷的天氣很適合喝熱茶放鬆」，系統可判斷「放鬆」為主要心情。讓系統在沒有明確心情關鍵字時，依據情境詞彙判斷最可能的心情。

   private MoodDetectionResult detectMoodFromContext(String input) {
       String lowerInput = input.toLowerCase();
   
       // 時間相關的心情推測
       if (lowerInput.contains("早上") || lowerInput.contains("morning")) {
           return new MoodDetectionResult("energetic", "早晨需要活力", 0.6);
       }
       if (lowerInput.contains("下午") || lowerInput.contains("afternoon")) {
           return new MoodDetectionResult("focused", "下午工作時光", 0.5);
       }
       if (lowerInput.contains("晚上") || lowerInput.contains("evening") || 
           lowerInput.contains("夜晚") || lowerInput.contains("night")) {
           return new MoodDetectionResult("relaxed", "夜晚放鬆時刻", 0.6);
       }
   
       // 天氣相關的心情推測
       if (lowerInput.contains("熱") || lowerInput.contains("夏天") || lowerInput.contains("hot")) {
           return new MoodDetectionResult("refreshed", "炎熱天氣", 0.5);
       }
       if (lowerInput.contains("冷") || lowerInput.contains("冬天") || lowerInput.contains("cold")) {
           return new MoodDetectionResult("cozy", "寒冷天氣", 0.5);
       }
   
       // 默認中性心情
       return new MoodDetectionResult("neutral", "中性心情", 0.3);
   }
   

3. 回傳心情類型、描述與信心度

   最終，當偵測到心情後，MoodDetectionService 會回傳一個結果物件，內容包含：

• 心情類型 mood（如「relaxed」、「tired」）

  • 心情描述 description（如「夜晚放鬆時刻」、「疲憊想休息」）
  • 信心度 confidence score，以 0~1 的小數點形式表示 ，並限制最大值為 1.0，確保信心度介於 0 到 1 之間，並以小數點表示。

  public static class MoodDetectionResult {
      private final String mood;
      private final String description;
      private final double confidence;
  
      public MoodDetectionResult(String mood, String description, double confidence) {
          this.mood = mood;
          this.description = description;
          this.confidence = confidence;
      }
  
      public String getMood() { return mood; }
      public String getDescription() { return description; }
      public double getConfidence() { return confidence; }
  }
  

運作流程：

1. 使用者輸入：「今天工作很累，想喝杯咖啡提神。」
2. 系統比對到「累」→ tired，並偵測到「工作」情境。
3. 回傳結果：心情類型「tired」，描述「工作後感到疲憊，建議喝提神飲品」，信心度 0.9。

總結來說，MoodDetectionService 讓系統能根據使用者輸入內容，回傳心情類型（mood）、心情描述（description）以及信心度（confidence），使推薦更符合使用者當下情緒，進一步提升互動體驗與推薦的準確度。

參考資料：

1. feelings - Vocabulary List _ Vocabulary.com
2. 情緒量表 - Character Lab _ Positive Education Hong Kong
3. 情緒儀表