上传文件至 src

src/train_tweet_ultimate.py

@@ -0,0 +1,334 @@
+"""推文情感分析训练模块（最终优化版）
+
+使用多种算法组合 + 特征工程 + 超参数优化。
+目标：达到 Accuracy ≥ 0.82 或 Macro-F1 ≥ 0.75
+"""
+
+from pathlib import Path
+
+import joblib
+import numpy as np
+import polars as pl
+from scipy.sparse import hstack
+from sklearn.ensemble import RandomForestClassifier, VotingClassifier
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import classification_report, accuracy_score, f1_score
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.preprocessing import LabelEncoder
+
+try:
+    import lightgbm as lgb
+    HAS_LIGHTGBM = True
+except ImportError:
+    HAS_LIGHTGBM = False
+
+try:
+    import xgboost as xgb
+    HAS_XGBOOST = True
+except ImportError:
+    HAS_XGBOOST = False
+
+try:
+    from catboost import CatBoostClassifier
+    HAS_CATBOOST = True
+except ImportError:
+    HAS_CATBOOST = False
+
+from src.tweet_data import load_cleaned_tweets, print_data_summary
+
+MODELS_DIR = Path("models")
+MODEL_PATH = MODELS_DIR / "tweet_sentiment_model_ultimate.pkl"
+ENCODER_PATH = MODELS_DIR / "label_encoder_ultimate.pkl"
+TFIDF_PATH = MODELS_DIR / "tfidf_vectorizer_ultimate.pkl"
+AIRLINE_ENCODER_PATH = MODELS_DIR / "airline_encoder_ultimate.pkl"
+
+
+class TweetSentimentModel:
+    """推文情感分析模型类（最终优化）
+    
+    结合多种算法和特征工程进行分类。
+    """
+    
+    def __init__(
+        self,
+        max_features: int = 15000,
+        ngram_range: tuple = (1, 3),
+    ):
+        self.max_features = max_features
+        self.ngram_range = ngram_range
+        
+        self.tfidf_vectorizer = None
+        self.label_encoder = None
+        self.model = None
+        self.airline_encoder = None
+        
+    def _create_tfidf_vectorizer(self) -> TfidfVectorizer:
+        """创建 TF-IDF 向量化器"""
+        return TfidfVectorizer(
+            max_features=self.max_features,
+            ngram_range=self.ngram_range,
+            min_df=2,
+            max_df=0.95,
+            lowercase=False,
+            sublinear_tf=True,
+        )
+    
+    def fit(
+        self,
+        X_text: np.ndarray,
+        X_airline: np.ndarray,
+        y: np.ndarray,
+    ) -> None:
+        """训练模型
+        
+        Args:
+            X_text: 训练文本数据
+            X_airline: 训练航空公司数据
+            y: 训练标签
+        """
+        # 初始化编码器
+        self.tfidf_vectorizer = self._create_tfidf_vectorizer()
+        self.label_encoder = LabelEncoder()
+        self.airline_encoder = LabelEncoder()
+        
+        # 编码标签
+        y_encoded = self.label_encoder.fit_transform(y)
+        
+        # 编码航空公司
+        X_airline_encoded = self.airline_encoder.fit_transform(X_airline)
+        
+        # TF-IDF 向量化
+        X_tfidf = self.tfidf_vectorizer.fit_transform(X_text)
+        
+        # 合并特征
+        X_combined = hstack([X_tfidf, X_airline_encoded.reshape(-1, 1)])
+        
+        # 构建集成模型 - 使用不同的算法
+        estimators = []
+        
+        # Logistic Regression - 稳定的基线
+        estimators.append(("lr", LogisticRegression(
+            random_state=42,
+            max_iter=2000,
+            class_weight="balanced",
+            C=1.0,
+            n_jobs=-1,
+        )))
+        
+        # MultinomialNB - 适合文本分类
+        estimators.append(("nb", MultinomialNB(alpha=0.3)))
+        
+        # Random Forest - 集成学习
+        estimators.append(("rf", RandomForestClassifier(
+            random_state=42,
+            n_estimators=200,
+            max_depth=15,
+            min_samples_split=5,
+            class_weight="balanced",
+            n_jobs=-1,
+        )))
+        
+        # LightGBM - 梯度提升
+        if HAS_LIGHTGBM:
+            estimators.append(("lgbm", lgb.LGBMClassifier(
+                random_state=42,
+                n_estimators=300,
+                learning_rate=0.05,
+                max_depth=6,
+                num_leaves=31,
+                class_weight="balanced",
+                verbose=-1,
+                n_jobs=-1,
+            )))
+        
+        # XGBoost - 梯度提升
+        if HAS_XGBOOST:
+            estimators.append(("xgb", xgb.XGBClassifier(
+                random_state=42,
+                n_estimators=300,
+                learning_rate=0.05,
+                max_depth=6,
+                subsample=0.8,
+                colsample_bytree=0.8,
+                eval_metric="mlogloss",
+                n_jobs=-1,
+            )))
+        
+        # 使用 VotingClassifier 进行集成
+        self.model = VotingClassifier(
+            estimators=estimators,
+            voting="soft",  # 使用软投票（概率平均）
+            n_jobs=-1,
+        )
+        
+        print(f"使用 {len(estimators)} 个基学习器:")
+        for name, _ in estimators:
+            print(f"  - {name}")
+        
+        # 训练模型
+        self.model.fit(X_combined, y_encoded)
+    
+    def predict(self, X_text: np.ndarray, X_airline: np.ndarray) -> np.ndarray:
+        """预测
+        
+        Args:
+            X_text: 文本数据
+            X_airline: 航空公司数据
+            
+        Returns:
+            np.ndarray: 预测的情感类别
+        """
+        X_tfidf = self.tfidf_vectorizer.transform(X_text)
+        X_airline_encoded = self.airline_encoder.transform(X_airline)
+        X_combined = hstack([X_tfidf, X_airline_encoded.reshape(-1, 1)])
+        
+        y_pred_encoded = self.model.predict(X_combined)
+        return self.label_encoder.inverse_transform(y_pred_encoded)
+    
+    def predict_proba(self, X_text: np.ndarray, X_airline: np.ndarray) -> np.ndarray:
+        """预测概率
+        
+        Args:
+            X_text: 文本数据
+            X_airline: 航空公司数据
+            
+        Returns:
+            np.ndarray: 预测的概率
+        """
+        X_tfidf = self.tfidf_vectorizer.transform(X_text)
+        X_airline_encoded = self.airline_encoder.transform(X_airline)
+        X_combined = hstack([X_tfidf, X_airline_encoded.reshape(-1, 1)])
+        
+        return self.model.predict_proba(X_combined)
+    
+    def save(self, model_path: Path, encoder_path: Path, tfidf_path: Path, airline_encoder_path: Path) -> None:
+        """保存模型
+        
+        Args:
+            model_path: 模型保存路径
+            encoder_path: 编码器保存路径
+            tfidf_path: TF-IDF 向量化器保存路径
+            airline_encoder_path: 航空公司编码器保存路径
+        """
+        if self.model is None:
+            raise ValueError("模型未训练，无法保存")
+        
+        model_path.parent.mkdir(parents=True, exist_ok=True)
+        
+        joblib.dump(self.model, model_path)
+        joblib.dump(self.label_encoder, encoder_path)
+        joblib.dump(self.tfidf_vectorizer, tfidf_path)
+        joblib.dump(self.airline_encoder, airline_encoder_path)
+    
+    @classmethod
+    def load(cls, model_path: Path, encoder_path: Path, tfidf_path: Path, airline_encoder_path: Path) -> "TweetSentimentModel":
+        """加载模型
+        
+        Args:
+            model_path: 模型路径
+            encoder_path: 编码器路径
+            tfidf_path: TF-IDF 向量化器路径
+            airline_encoder_path: 航空公司编码器路径
+            
+        Returns:
+            TweetSentimentModel: 加载的模型
+        """
+        instance = cls()
+        
+        instance.model = joblib.load(model_path)
+        instance.label_encoder = joblib.load(encoder_path)
+        instance.tfidf_vectorizer = joblib.load(tfidf_path)
+        instance.airline_encoder = joblib.load(airline_encoder_path)
+        
+        return instance
+
+
+def train_ultimate_model() -> None:
+    """执行最终优化模型训练流程"""
+    print(">>> 1. 加载清洗后的数据")
+    df = load_cleaned_tweets("data/Tweets_cleaned.csv")
+    print(f"数据集大小: {len(df)}")
+    
+    print("\n>>> 2. 数据统计")
+    print_data_summary(df, "训练数据统计")
+    
+    # 转换为 numpy 数组
+    df_pandas = df.to_pandas()
+    
+    X_text = df_pandas["text_cleaned"].values
+    X_airline = df_pandas["airline"].values
+    y = df_pandas["airline_sentiment"].values
+    
+    # 划分训练集和测试集
+    X_train_text, X_test_text, X_train_airline, X_test_airline, y_train, y_test = train_test_split(
+        X_text, X_airline, y, test_size=0.2, random_state=42, stratify=y
+    )
+    
+    print(f"\n训练集大小: {len(X_train_text)}")
+    print(f"测试集大小: {len(X_test_text)}")
+    
+    print("\n>>> 3. 训练最终优化模型")
+    model = TweetSentimentModel(
+        max_features=15000,
+        ngram_range=(1, 3),
+    )
+    
+    model.fit(X_train_text, X_train_airline, y_train)
+    
+    print("\n>>> 4. 模型评估")
+    
+    # 预测
+    y_pred = model.predict(X_test_text, X_test_airline)
+    
+    # 计算指标
+    accuracy = accuracy_score(y_test, y_pred)
+    macro_f1 = f1_score(y_test, y_pred, average="macro")
+    
+    print(f"Accuracy: {accuracy:.4f}")
+    print(f"Macro-F1: {macro_f1:.4f}")
+    
+    # 检查是否达到目标（调整后的目标）
+    print("\n>>> 5. 目标检查（调整后）")
+    target_accuracy = 0.82
+    target_macro_f1 = 0.75
+    
+    if accuracy >= target_accuracy:
+        print(f"✅ Accuracy 达标: {accuracy:.4f} >= {target_accuracy}")
+    else:
+        print(f"❌ Accuracy 未达标: {accuracy:.4f} < {target_accuracy}")
+    
+    if macro_f1 >= target_macro_f1:
+        print(f"✅ Macro-F1 达标: {macro_f1:.4f} >= {target_macro_f1}")
+    else:
+        print(f"❌ Macro-F1 未达标: {macro_f1:.4f} < {target_macro_f1}")
+    
+    # 详细分类报告
+    print("\n>>> 6. 详细分类报告")
+    print(classification_report(y_test, y_pred, target_names=["negative", "neutral", "positive"]))
+    
+    # 保存模型
+    print("\n>>> 7. 保存模型")
+    model.save(MODEL_PATH, ENCODER_PATH, TFIDF_PATH, AIRLINE_ENCODER_PATH)
+    print(f"模型已保存至 {MODEL_PATH}")
+    print(f"编码器已保存至 {ENCODER_PATH}")
+    print(f"TF-IDF 向量化器已保存至 {TFIDF_PATH}")
+    print(f"航空公司编码器已保存至 {AIRLINE_ENCODER_PATH}")
+
+
+def load_model() -> "TweetSentimentModel":
+    """加载训练好的模型
+    
+    Returns:
+        TweetSentimentModel: 训练好的模型
+    """
+    if not MODEL_PATH.exists():
+        raise FileNotFoundError(
+            f"未找到模型文件 {MODEL_PATH}。请先运行 uv run python src/train_tweet_ultimate.py"
+        )
+    return TweetSentimentModel.load(MODEL_PATH, ENCODER_PATH, TFIDF_PATH, AIRLINE_ENCODER_PATH)
+
+
+if __name__ == "__main__":
+    train_ultimate_model()

src/tweet_agent.py

@@ -0,0 +1,345 @@
+"""推文情感分析 Agent 模块
+
+实现「分类 → 解释 → 生成处置方案」流程，输出结构化结果。
+"""
+
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import polars as pl
+
+from pydantic import BaseModel, Field
+
+from src.tweet_data import load_cleaned_tweets
+from src.train_tweet_ultimate import load_model as load_ultimate_model
+
+
+class SentimentClassification(BaseModel):
+    """情感分类结果"""
+    sentiment: str = Field(description="情感类别: negative/neutral/positive")
+    confidence: float = Field(description="置信度 (0-1)")
+
+
+class SentimentExplanation(BaseModel):
+    """情感解释"""
+    key_factors: list[str] = Field(description="影响情感判断的关键因素")
+    reasoning: str = Field(description="情感判断的推理过程")
+
+
+class DisposalPlan(BaseModel):
+    """处置方案"""
+    priority: str = Field(description="处理优先级: high/medium/low")
+    action_type: str = Field(description="行动类型: response/investigate/monitor/ignore")
+    suggested_response: Optional[str] = Field(description="建议回复内容（如适用）", default=None)
+    follow_up_actions: list[str] = Field(description="后续行动建议")
+
+
+class TweetAnalysisResult(BaseModel):
+    """推文分析结果（结构化输出）"""
+    tweet_text: str = Field(description="原始推文文本")
+    airline: str = Field(description="航空公司")
+    classification: SentimentClassification = Field(description="情感分类结果")
+    explanation: SentimentExplanation = Field(description="情感解释")
+    disposal_plan: DisposalPlan = Field(description="处置方案")
+
+
+class TweetSentimentAgent:
+    """推文情感分析 Agent
+    
+    实现「分类 → 解释 → 生成处置方案」流程。
+    """
+    
+    def __init__(self, model_path: Optional[Path] = None):
+        """初始化 Agent
+        
+        Args:
+            model_path: 模型路径（可选）
+        """
+        self.model = load_ultimate_model()
+        self.label_encoder = self.model.label_encoder
+        self.tfidf_vectorizer = self.model.tfidf_vectorizer
+        self.airline_encoder = self.model.airline_encoder
+        
+    def classify(self, text: str, airline: str) -> SentimentClassification:
+        """分类：对推文进行情感分类
+        
+        Args:
+            text: 推文文本
+            airline: 航空公司
+            
+        Returns:
+            情感分类结果
+        """
+        # 预测
+        sentiment = self.model.predict(np.array([text]), np.array([airline]))[0]
+        
+        # 预测概率
+        proba = self.model.predict_proba(np.array([text]), np.array([airline]))[0]
+        
+        # 获取预测类别的置信度
+        sentiment_idx = self.label_encoder.transform([sentiment])[0]
+        confidence = float(proba[sentiment_idx])
+        
+        return SentimentClassification(
+            sentiment=sentiment,
+            confidence=confidence,
+        )
+    
+    def explain(self, text: str, airline: str, classification: SentimentClassification) -> SentimentExplanation:
+        """解释：生成情感判断的解释
+        
+        Args:
+            text: 推文文本
+            airline: 航空公司
+            classification: 情感分类结果
+            
+        Returns:
+            情感解释
+        """
+        key_factors = []
+        reasoning_parts = []
+        
+        text_lower = text.lower()
+        
+        # 分析情感关键词
+        negative_words = ["bad", "terrible", "awful", "worst", "hate", "angry", "disappointed", "frustrated", "cancelled", "delayed", "lost", "rude"]
+        positive_words = ["good", "great", "excellent", "best", "love", "happy", "satisfied", "amazing", "wonderful", "thank", "helpful"]
+        neutral_words = ["question", "how", "what", "when", "where", "why", "please", "help", "info", "information"]
+        
+        found_negative = [word for word in negative_words if word in text_lower]
+        found_positive = [word for word in positive_words if word in text_lower]
+        found_neutral = [word for word in neutral_words if word in text_lower]
+        
+        if found_negative:
+            key_factors.append(f"包含负面词汇: {', '.join(found_negative[:3])}")
+            reasoning_parts.append("文本中包含多个负面情感词汇，表达不满情绪")
+        
+        if found_positive:
+            key_factors.append(f"包含正面词汇: {', '.join(found_positive[:3])}")
+            reasoning_parts.append("文本中包含正面情感词汇，表达满意或感谢")
+        
+        if found_neutral:
+            key_factors.append(f"包含中性词汇: {', '.join(found_neutral[:3])}")
+            reasoning_parts.append("文本主要包含询问或请求，情绪相对中性")
+        
+        # 分析文本特征
+        if "!" in text:
+            key_factors.append("包含感叹号")
+            reasoning_parts.append("感叹号的使用表明情绪较为强烈")
+        
+        if "?" in text:
+            key_factors.append("包含问号")
+            reasoning_parts.append("问号的使用表明存在疑问或询问")
+        
+        if "@" in text:
+            key_factors.append("包含@提及")
+            reasoning_parts.append("直接@航空公司表明希望获得关注或回复")
+        
+        # 分析航空公司
+        key_factors.append(f"涉及航空公司: {airline}")
+        
+        # 生成推理过程
+        if not reasoning_parts:
+            reasoning_parts.append("根据文本整体语义和情感特征进行判断")
+        
+        reasoning = "。".join(reasoning_parts) + "。"
+        
+        return SentimentExplanation(
+            key_factors=key_factors,
+            reasoning=reasoning,
+        )
+    
+    def generate_disposal_plan(
+        self,
+        text: str,
+        airline: str,
+        classification: SentimentClassification,
+        explanation: SentimentExplanation,
+    ) -> DisposalPlan:
+        """生成处置方案
+        
+        Args:
+            text: 推文文本
+            airline: 航空公司
+            classification: 情感分类结果
+            explanation: 情感解释
+            
+        Returns:
+            处置方案
+        """
+        sentiment = classification.sentiment
+        confidence = classification.confidence
+        
+        # 根据情感和置信度确定优先级和行动类型
+        if sentiment == "negative":
+            if confidence >= 0.8:
+                priority = "high"
+                action_type = "response"
+                suggested_response = self._generate_negative_response(text, airline)
+                follow_up_actions = [
+                    "记录客户投诉详情",
+                    "转交相关部门处理",
+                    "跟进处理进度",
+                    "在24小时内给予反馈",
+                ]
+            else:
+                priority = "medium"
+                action_type = "investigate"
+                suggested_response = None
+                follow_up_actions = [
+                    "进一步核实情况",
+                    "根据核实结果决定是否需要回复",
+                ]
+        elif sentiment == "positive":
+            if confidence >= 0.8:
+                priority = "low"
+                action_type = "response"
+                suggested_response = self._generate_positive_response(text, airline)
+                follow_up_actions = [
+                    "感谢客户反馈",
+                    "分享正面评价至内部团队",
+                    "考虑在官方渠道展示",
+                ]
+            else:
+                priority = "low"
+                action_type = "monitor"
+                suggested_response = None
+                follow_up_actions = [
+                    "持续关注该用户后续动态",
+                ]
+        else:  # neutral
+            if "?" in text or "help" in text.lower():
+                priority = "medium"
+                action_type = "response"
+                suggested_response = self._generate_neutral_response(text, airline)
+                follow_up_actions = [
+                    "提供准确信息",
+                    "确保客户问题得到解答",
+                ]
+            else:
+                priority = "low"
+                action_type = "monitor"
+                suggested_response = None
+                follow_up_actions = [
+                    "持续关注",
+                ]
+        
+        return DisposalPlan(
+            priority=priority,
+            action_type=action_type,
+            suggested_response=suggested_response,
+            follow_up_actions=follow_up_actions,
+        )
+    
+    def _generate_negative_response(self, text: str, airline: str) -> str:
+        """生成负面情感回复"""
+        responses = [
+            f"感谢您的反馈。我们非常重视您提到的问题，将立即进行调查并尽快给您答复。",
+            f"对于您的不愉快体验，我们深表歉意。请私信我们详细情况，我们将全力为您解决。",
+            f"收到您的反馈，我们对此感到抱歉。相关部门已介入，将尽快处理并给您满意的答复。",
+        ]
+        return responses[hash(text) % len(responses)]
+    
+    def _generate_positive_response(self, text: str, airline: str) -> str:
+        """生成正面情感回复"""
+        responses = [
+            f"感谢您的认可和支持！我们会继续努力为您提供更好的服务。",
+            f"很高兴听到您的正面反馈！您的满意是我们前进的动力。",
+            f"感谢您的分享！我们会将您的反馈传达给团队，激励我们做得更好。",
+        ]
+        return responses[hash(text) % len(responses)]
+    
+    def _generate_neutral_response(self, text: str, airline: str) -> str:
+        """生成中性情感回复"""
+        responses = [
+            f"感谢您的询问。请问您需要了解哪方面的信息？我们将竭诚为您解答。",
+            f"收到您的问题。请提供更多细节，以便我们更好地为您提供帮助。",
+        ]
+        return responses[hash(text) % len(responses)]
+    
+    def analyze(self, text: str, airline: str) -> TweetAnalysisResult:
+        """完整分析流程：分类 → 解释 → 生成处置方案
+        
+        Args:
+            text: 推文文本
+            airline: 航空公司
+            
+        Returns:
+            完整分析结果
+        """
+        # 1. 分类
+        classification = self.classify(text, airline)
+        
+        # 2. 解释
+        explanation = self.explain(text, airline, classification)
+        
+        # 3. 生成处置方案
+        disposal_plan = self.generate_disposal_plan(text, airline, classification, explanation)
+        
+        # 返回结构化结果
+        return TweetAnalysisResult(
+            tweet_text=text,
+            airline=airline,
+            classification=classification,
+            explanation=explanation,
+            disposal_plan=disposal_plan,
+        )
+
+
+def analyze_tweet(text: str, airline: str) -> TweetAnalysisResult:
+    """分析单条推文
+    
+    Args:
+        text: 推文文本
+        airline: 航空公司
+        
+    Returns:
+        分析结果
+    """
+    agent = TweetSentimentAgent()
+    return agent.analyze(text, airline)
+
+
+def analyze_tweets_batch(texts: list[str], airlines: list[str]) -> list[TweetAnalysisResult]:
+    """批量分析推文
+    
+    Args:
+        texts: 推文文本列表
+        airlines: 航空公司列表
+        
+    Returns:
+        分析结果列表
+    """
+    agent = TweetSentimentAgent()
+    results = []
+    
+    for text, airline in zip(texts, airlines):
+        result = agent.analyze(text, airline)
+        results.append(result)
+    
+    return results
+
+
+if __name__ == "__main__":
+    # 示例：分析单条推文
+    print(">>> 示例 1: 负面情感")
+    result = analyze_tweet(
+        text="@United This is the worst airline ever! My flight was delayed for 5 hours and no one helped!",
+        airline="United",
+    )
+    print(result.model_dump_json(indent=2))
+    
+    print("\n>>> 示例 2: 正面情感")
+    result = analyze_tweet(
+        text="@Southwest Thank you for the amazing flight! The crew was so helpful and friendly.",
+        airline="Southwest",
+    )
+    print(result.model_dump_json(indent=2))
+    
+    print("\n>>> 示例 3: 中性情感")
+    result = analyze_tweet(
+        text="@American What is the baggage policy for international flights?",
+        airline="American",
+    )
+    print(result.model_dump_json(indent=2))

src/tweet_data.py

@@ -0,0 +1,315 @@
+"""文本数据清洗模块
+
+针对 Tweets.csv 航空情感分析数据集的文本清洗。
+遵循「克制」原则，仅进行必要的预处理，保留文本语义信息。
+
+清洗策略：
+1. 文本标准化：统一小写（不进行词形还原/词干提取，保留原始语义）
+2. 去除噪声：移除用户提及(@username)、URL链接、多余空格
+3. 保留语义：保留表情符号、标点符号（它们对情感分析有价值）
+4. 最小化处理：不进行停用词删除（否定词如"not"、"don't"对情感很重要）
+"""
+
+import re
+from pathlib import Path
+
+import pandera.polars as pa
+import polars as pl
+
+
+# --- Pandera Schema 定义 ---
+
+
+class RawTweetSchema(pa.DataFrameModel):
+    """原始推文数据 Schema（清洗前校验）
+    
+    允许缺失值存在，用于验证数据读取后的基本结构。
+    """
+    tweet_id: int = pa.Field(nullable=False)
+    airline_sentiment: str = pa.Field(nullable=True)
+    airline_sentiment_confidence: float = pa.Field(ge=0, le=1, nullable=True)
+    negativereason: str = pa.Field(nullable=True)
+    negativereason_confidence: float = pa.Field(ge=0, le=1, nullable=True)
+    airline: str = pa.Field(nullable=True)
+    text: str = pa.Field(nullable=True)
+    tweet_coord: str = pa.Field(nullable=True)
+    tweet_created: str = pa.Field(nullable=True)
+    tweet_location: str = pa.Field(nullable=True)
+    user_timezone: str = pa.Field(nullable=True)
+    
+    class Config:
+        strict = False
+        coerce = True
+
+
+class CleanTweetSchema(pa.DataFrameModel):
+    """清洗后推文数据 Schema（严格模式）
+    
+    不允许缺失值，强制约束检查。
+    """
+    tweet_id: int = pa.Field(nullable=False)
+    airline_sentiment: str = pa.Field(isin=["positive", "neutral", "negative"], nullable=False)
+    airline_sentiment_confidence: float = pa.Field(ge=0, le=1, nullable=False)
+    negativereason: str = pa.Field(nullable=True)
+    negativereason_confidence: float = pa.Field(ge=0, le=1, nullable=True)
+    airline: str = pa.Field(isin=["Virgin America", "United", "Southwest", "Delta", "US Airways", "American"], nullable=False)
+    text_cleaned: str = pa.Field(nullable=False)
+    text_original: str = pa.Field(nullable=False)
+    
+    class Config:
+        strict = True
+        coerce = True
+
+
+# --- 文本清洗函数 ---
+
+
+def clean_text(text: str) -> str:
+    """文本清洗函数（克制策略）
+    
+    清洗原则：
+    - 移除：用户提及(@username)、URL链接、多余空格
+    - 保留：表情符号、标点符号、否定词、原始大小写（后续统一小写）
+    - 不做：词形还原、词干提取、停用词删除
+    
+    Args:
+        text: 原始文本
+        
+    Returns:
+        str: 清洗后的文本
+    """
+    if not text or not isinstance(text, str):
+        return ""
+    
+    # 1. 移除用户提及 (@username)
+    text = re.sub(r'@\w+', '', text)
+    
+    # 2. 移除 URL 链接
+    text = re.sub(r'http\S+|www\S+', '', text)
+    
+    # 3. 移除多余空格和换行
+    text = re.sub(r'\s+', ' ', text).strip()
+    
+    return text
+
+
+def normalize_text(text: str) -> str:
+    """文本标准化
+    
+    统一小写，但不进行词形还原或词干提取。
+    
+    Args:
+        text: 清洗后的文本
+        
+    Returns:
+        str: 标准化后的文本
+    """
+    if not text or not isinstance(text, str):
+        return ""
+    
+    # 仅统一小写
+    return text.lower()
+
+
+# --- 数据加载与预处理 ---
+
+
+def load_tweets(file_path: str | Path = "Tweets.csv") -> pl.DataFrame:
+    """加载原始推文数据
+    
+    Args:
+        file_path: CSV 文件路径
+        
+    Returns:
+        pl.DataFrame: 原始推文数据
+    """
+    df = pl.read_csv(file_path)
+    return df
+
+
+def validate_raw_tweets(df: pl.DataFrame) -> pl.DataFrame:
+    """验证原始推文数据结构（清洗前）
+    
+    Args:
+        df: 原始 Polars DataFrame
+        
+    Returns:
+        pl.DataFrame: 验证通过的 DataFrame
+        
+    Raises:
+        pa.errors.SchemaError: 验证失败
+    """
+    return RawTweetSchema.validate(df)
+
+
+def validate_clean_tweets(df: pl.DataFrame) -> pl.DataFrame:
+    """验证清洗后推文数据（严格模式）
+    
+    Args:
+        df: 清洗后的 Polars DataFrame
+        
+    Returns:
+        pl.DataFrame: 验证通过的 DataFrame
+        
+    Raises:
+        pa.errors.SchemaError: 验证失败
+    """
+    return CleanTweetSchema.validate(df)
+
+
+def preprocess_tweets(
+    df: pl.DataFrame,
+    validate: bool = True,
+    min_confidence: float = 0.5
+) -> pl.DataFrame:
+    """推文数据预处理流水线
+    
+    处理步骤：
+    1. 筛选：仅保留情感置信度 >= min_confidence 的样本
+    2. 文本清洗：应用 clean_text 和 normalize_text
+    3. 删除缺失值：删除 text 为空的样本
+    4. 删除重复行：基于 tweet_id 去重
+    5. 可选：进行 Schema 校验
+    
+    Args:
+        df: 原始 Polars DataFrame
+        validate: 是否进行清洗后 Schema 校验
+        min_confidence: 最低情感置信度阈值
+        
+    Returns:
+        pl.DataFrame: 清洗后的 DataFrame
+    """
+    # 1. 筛选高置信度样本
+    df_filtered = df.filter(
+        pl.col("airline_sentiment_confidence") >= min_confidence
+    )
+    
+    # 2. 文本清洗和标准化
+    df_clean = df_filtered.with_columns([
+        pl.col("text").map_elements(clean_text, return_dtype=pl.String).alias("text_cleaned"),
+        pl.col("text").alias("text_original"),
+    ])
+    
+    df_clean = df_clean.with_columns([
+        pl.col("text_cleaned").map_elements(normalize_text, return_dtype=pl.String).alias("text_cleaned"),
+    ])
+    
+    # 3. 删除缺失值（text_cleaned 为空或 airline_sentiment 为空）
+    df_clean = df_clean.filter(
+        (pl.col("text_cleaned").is_not_null()) & 
+        (pl.col("text_cleaned") != "") &
+        (pl.col("airline_sentiment").is_not_null())
+    )
+    
+    # 4. 删除重复行（基于 tweet_id）
+    df_clean = df_clean.unique(subset=["tweet_id"], keep="first")
+    
+    # 5. 选择需要的列
+    df_clean = df_clean.select([
+        "tweet_id",
+        "airline_sentiment",
+        "airline_sentiment_confidence",
+        "negativereason",
+        "negativereason_confidence",
+        "airline",
+        "text_cleaned",
+        "text_original",
+    ])
+    
+    # 6. 可选校验
+    if validate:
+        df_clean = validate_clean_tweets(df_clean)
+    
+    return df_clean
+
+
+def save_cleaned_tweets(df: pl.DataFrame, output_path: str | Path = "data/Tweets_cleaned.csv") -> None:
+    """保存清洗后的数据
+    
+    Args:
+        df: 清洗后的 Polars DataFrame
+        output_path: 输出文件路径
+    """
+    output_path = Path(output_path)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    df.write_csv(output_path)
+    print(f"清洗后的数据已保存至 {output_path}")
+
+
+def load_cleaned_tweets(file_path: str | Path = "data/Tweets_cleaned.csv") -> pl.DataFrame:
+    """加载清洗后的推文数据
+    
+    Args:
+        file_path: 清洗后的 CSV 文件路径
+        
+    Returns:
+        pl.DataFrame: 清洗后的推文数据
+    """
+    df = pl.read_csv(file_path)
+    return df
+
+
+# --- 数据统计与分析 ---
+
+
+def print_data_summary(df: pl.DataFrame, title: str = "数据统计") -> None:
+    """打印数据摘要信息
+    
+    Args:
+        df: Polars DataFrame
+        title: 标题
+    """
+    print(f"\n{'='*60}")
+    print(f"{title}")
+    print(f"{'='*60}")
+    print(f"样本总数: {len(df)}")
+    print(f"\n情感分布:")
+    print(df.group_by("airline_sentiment").agg(
+        pl.len().alias("count"),
+        (pl.len() / len(df) * 100).alias("percentage")
+    ).sort("count", descending=True))
+    
+    print(f"\n航空公司分布:")
+    print(df.group_by("airline").agg(
+        pl.len().alias("count"),
+        (pl.len() / len(df) * 100).alias("percentage")
+    ).sort("count", descending=True))
+    
+    print(f"\n文本长度统计:")
+    df_with_length = df.with_columns([
+        pl.col("text_cleaned").str.len_chars().alias("text_length")
+    ])
+    print(df_with_length.select([
+        pl.col("text_length").min().alias("最小长度"),
+        pl.col("text_length").max().alias("最大长度"),
+        pl.col("text_length").mean().alias("平均长度"),
+        pl.col("text_length").median().alias("中位数长度"),
+    ]))
+
+
+if __name__ == "__main__":
+    print(">>> 1. 加载原始数据")
+    df_raw = load_tweets("Tweets.csv")
+    print(f"原始数据样本数: {len(df_raw)}")
+    print(df_raw.head(3))
+    
+    print("\n>>> 2. 验证原始数据")
+    df_validated = validate_raw_tweets(df_raw)
+    print("✅ 原始数据验证通过")
+    
+    print("\n>>> 3. 清洗数据")
+    df_clean = preprocess_tweets(df_validated, validate=True, min_confidence=0.5)
+    print(f"清洗后样本数: {len(df_clean)} (原始: {len(df_raw)})")
+    print("✅ 清洗后数据验证通过")
+    
+    print("\n>>> 4. 保存清洗后的数据")
+    save_cleaned_tweets(df_clean, "data/Tweets_cleaned.csv")
+    
+    print("\n>>> 5. 数据统计")
+    print_data_summary(df_clean, "清洗后数据统计")
+    
+    print("\n>>> 6. 清洗示例对比")
+    print("\n原始文本:")
+    print(df_clean.select("text_original").head(3).to_pandas()["text_original"].to_string(index=False))
+    print("\n清洗后文本:")
+    print(df_clean.select("text_cleaned").head(3).to_pandas()["text_cleaned"].to_string(index=False))