上传文件至 src

src/__init__.py

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+"""推文情感分析包"""
+
+from src.tweet_agent import TweetSentimentAgent, analyze_tweet
+from src.tweet_data import load_cleaned_tweets
+from src.train_tweet_ultimate import load_model
+
+__all__ = [
+    "TweetSentimentAgent",
+    "analyze_tweet",
+    "load_cleaned_tweets",
+    "load_model",
+]

src/streamlit_tweet_app.py

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+"""Streamlit 演示应用 - 推文情感分析
+
+航空推文情感分析 AI 助手 - 支持情感分类、解释和处置方案生成。
+"""
+
+import os
+import sys
+
+import streamlit as st
+
+from dotenv import load_dotenv
+
+from src.tweet_agent import TweetSentimentAgent, analyze_tweet
+
+# Load env variables
+load_dotenv()
+
+st.set_page_config(page_title="航空推文情感分析", page_icon="✈️", layout="wide")
+
+# Sidebar Configuration
+st.sidebar.header("🔧 配置")
+st.sidebar.markdown("### 模型信息")
+st.sidebar.info(
+    """
+    **模型**: VotingClassifier (5个基学习器)
+    - Logistic Regression
+    - Multinomial Naive Bayes
+    - Random Forest
+    - LightGBM
+    - XGBoost
+    
+    **性能**: Macro-F1 = 0.7533 ✅
+    """
+)
+
+st.sidebar.markdown("---")
+# Mode Selection
+mode = st.sidebar.radio("功能选择", ["📝 单条分析", "📊 批量分析", "📈 数据概览"])
+
+# Initialize session state
+if "agent" not in st.session_state:
+    with st.spinner("🔄 加载模型..."):
+        st.session_state.agent = TweetSentimentAgent()
+
+if "batch_results" not in st.session_state:
+    st.session_state.batch_results = []
+
+
+# --- Helper Functions ---
+
+
+def get_sentiment_emoji(sentiment: str) -> str:
+    """获取情感对应的表情符号"""
+    emoji_map = {
+        "negative": "😠",
+        "neutral": "😐",
+        "positive": "😊",
+    }
+    return emoji_map.get(sentiment, "❓")
+
+
+def get_sentiment_color(sentiment: str) -> str:
+    """获取情感对应的颜色"""
+    color_map = {
+        "negative": "#ff6b6b",
+        "neutral": "#ffd93d",
+        "positive": "#6bcb77",
+    }
+    return color_map.get(sentiment, "#e0e0e0")
+
+
+def get_priority_color(priority: str) -> str:
+    """获取优先级对应的颜色"""
+    color_map = {
+        "high": "#ff4757",
+        "medium": "#ffa502",
+        "low": "#2ed573",
+    }
+    return color_map.get(priority, "#e0e0e0")
+
+
+# --- Main Views ---
+
+if mode == "📝 单条分析":
+    st.title("✈️ 航空推文情感分析")
+    st.markdown("输入推文文本，获取 AI 驱动的情感分析、解释和处置方案。")
+
+    # Input form
+    with st.form("tweet_analysis_form"):
+        col1, col2 = st.columns([3, 1])
+        
+        with col1:
+            tweet_text = st.text_area(
+                "推文内容",
+                placeholder="@United This is the worst airline ever! My flight was delayed for 5 hours...",
+                height=100,
+            )
+        
+        with col2:
+            airline = st.selectbox(
+                "航空公司",
+                ["United", "US Airways", "American", "Southwest", "Delta", "Virgin America"],
+            )
+        
+        submitted = st.form_submit_button("🔍 分析", type="primary")
+
+    if submitted and tweet_text:
+        with st.spinner("🤖 AI 正在分析..."):
+            try:
+                result = analyze_tweet(tweet_text, airline)
+                
+                # Display results
+                st.divider()
+                
+                # Header with sentiment
+                sentiment_emoji = get_sentiment_emoji(result.classification.sentiment)
+                sentiment_color = get_sentiment_color(result.classification.sentiment)
+                
+                st.markdown(
+                    f"""
+                    <div style="background-color: {sentiment_color}; padding: 20px; border-radius: 10px; text-align: center;">
+                        <h1 style="color: white; margin: 0;">{sentiment_emoji} {result.classification.sentiment.upper()}</h1>
+                        <p style="color: white; margin: 10px 0 0 0;">置信度: {result.classification.confidence:.1%}</p>
+                    </div>
+                    """,
+                    unsafe_allow_html=True,
+                )
+                
+                st.divider()
+                
+                # Original tweet
+                st.subheader("📝 原始推文")
+                st.info(f"**航空公司**: {result.airline}\n\n**内容**: {result.tweet_text}")
+                
+                # Explanation
+                st.subheader("🔍 情感解释")
+                st.markdown("**关键因素:**")
+                for factor in result.explanation.key_factors:
+                    st.write(f"- {factor}")
+                
+                st.markdown("**推理过程:**")
+                st.write(result.explanation.reasoning)
+                
+                # Disposal plan
+                st.subheader("📋 处置方案")
+                
+                priority_color = get_priority_color(result.disposal_plan.priority)
+                st.markdown(
+                    f"""
+                    <div style="background-color: {priority_color}; padding: 10px; border-radius: 5px; display: inline-block;">
+                        <span style="color: white; font-weight: bold;">优先级: {result.disposal_plan.priority.upper()}</span>
+                    </div>
+                    <br><br>
+                    **行动类型**: {result.disposal_plan.action_type}
+                    """,
+                    unsafe_allow_html=True,
+                )
+                
+                if result.disposal_plan.suggested_response:
+                    st.markdown("**建议回复:**")
+                    st.success(result.disposal_plan.suggested_response)
+                
+                st.markdown("**后续行动:**")
+                for action in result.disposal_plan.follow_up_actions:
+                    st.write(f"- {action}")
+                
+            except Exception as e:
+                st.error(f"分析失败: {e!s}")
+
+elif mode == "📊 批量分析":
+    st.title("📊 批量推文分析")
+    st.markdown("上传 CSV 文件或输入多条推文，进行批量情感分析。")
+    
+    # Input method selection
+    input_method = st.radio("输入方式", ["手动输入", "CSV 上传"], horizontal=True)
+    
+    if input_method == "手动输入":
+        st.markdown("### 输入推文（每行一条）")
+        tweets_input = st.text_area(
+            "推文列表",
+            placeholder="@United Flight delayed again!\n@Southwest Great service!\n@American Baggage policy?",
+            height=200,
+        )
+        
+        if st.button("🔍 批量分析", type="primary") and tweets_input:
+            lines = [line.strip() for line in tweets_input.split("\n") if line.strip()]
+            
+            if lines:
+                with st.spinner("🤖 AI 正在分析..."):
+                    results = []
+                    for line in lines:
+                        try:
+                            # Extract airline from tweet (simple heuristic)
+                            airline = "United"  # Default
+                            for a in ["United", "US Airways", "American", "Southwest", "Delta", "Virgin America"]:
+                                if a.lower() in line.lower():
+                                    airline = a
+                                    break
+                            
+                            result = analyze_tweet(line, airline)
+                            results.append(result)
+                        except Exception as e:
+                            st.warning(f"分析失败: {line[:50]}... - {e}")
+                    
+                    if results:
+                        st.session_state.batch_results = results
+                        st.success(f"✅ 成功分析 {len(results)} 条推文")
+    
+    else:  # CSV upload
+        st.markdown("### 上传 CSV 文件")
+        st.info("CSV 文件应包含以下列: `text` (推文内容), `airline` (航空公司)")
+        
+        uploaded_file = st.file_uploader("选择文件", type=["csv"])
+        
+        if uploaded_file and st.button("🔍 分析上传文件", type="primary"):
+            import pandas as pd
+            
+            try:
+                df = pd.read_csv(uploaded_file)
+                
+                if "text" not in df.columns:
+                    st.error("CSV 文件必须包含 'text' 列")
+                else:
+                    with st.spinner("🤖 AI 正在分析..."):
+                        results = []
+                        for _, row in df.iterrows():
+                            try:
+                                text = row["text"]
+                                airline = row.get("airline", "United")
+                                result = analyze_tweet(text, airline)
+                                results.append(result)
+                            except Exception as e:
+                                st.warning(f"分析失败: {text[:50]}... - {e}")
+                        
+                        if results:
+                            st.session_state.batch_results = results
+                            st.success(f"✅ 成功分析 {len(results)} 条推文")
+            
+            except Exception as e:
+                st.error(f"文件读取失败: {e!s}")
+    
+    # Display batch results
+    if st.session_state.batch_results:
+        st.divider()
+        st.subheader(f"📊 分析结果 ({len(st.session_state.batch_results)} 条)")
+        
+        # Summary statistics
+        sentiments = [r.classification.sentiment for r in st.session_state.batch_results]
+        negative_count = sentiments.count("negative")
+        neutral_count = sentiments.count("neutral")
+        positive_count = sentiments.count("positive")
+        
+        col1, col2, col3 = st.columns(3)
+        col1.metric("😠 负面", negative_count)
+        col2.metric("😐 中性", neutral_count)
+        col3.metric("😊 正面", positive_count)
+        
+        # Detailed results table
+        st.markdown("### 详细结果")
+        
+        results_data = []
+        for r in st.session_state.batch_results:
+            results_data.append({
+                "推文": r.tweet_text[:50] + "..." if len(r.tweet_text) > 50 else r.tweet_text,
+                "航空公司": r.airline,
+                "情感": f"{get_sentiment_emoji(r.classification.sentiment)} {r.classification.sentiment}",
+                "置信度": f"{r.classification.confidence:.1%}",
+                "优先级": r.disposal_plan.priority.upper(),
+                "行动类型": r.disposal_plan.action_type,
+            })
+        
+        st.dataframe(results_data, use_container_width=True)
+        
+        # Clear button
+        if st.button("🗑️ 清除结果"):
+            st.session_state.batch_results = []
+            st.rerun()
+
+elif mode == "📈 数据概览":
+    st.title("📈 数据集概览")
+    st.markdown("查看训练数据集的统计信息。")
+    
+    try:
+        import polars as pl
+        from src.tweet_data import load_cleaned_tweets, print_data_summary
+        
+        df = load_cleaned_tweets("data/Tweets_cleaned.csv")
+        
+        # Display summary
+        st.subheader("📊 数据统计")
+        print_data_summary(df, "数据集统计")
+        
+        # Display sample data
+        st.subheader("📝 样本数据")
+        sample_df = df.head(10).to_pandas()
+        st.dataframe(sample_df, use_container_width=True)
+        
+        # Sentiment distribution chart
+        st.subheader("📈 情感分布")
+        sentiment_counts = df.group_by("airline_sentiment").agg(
+            pl.col("airline_sentiment").count().alias("count")
+        ).sort("count", descending=True)
+        
+        import pandas as pd
+        import plotly.express as px
+        
+        sentiment_df = sentiment_counts.to_pandas()
+        fig = px.pie(
+            sentiment_df,
+            values="count",
+            names="airline_sentiment",
+            title="情感分布",
+            color_discrete_map={
+                "negative": "#ff6b6b",
+                "neutral": "#ffd93d",
+                "positive": "#6bcb77",
+            },
+        )
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # Airline distribution chart
+        st.subheader("✈️ 航空公司分布")
+        airline_counts = df.group_by("airline").agg(
+            pl.col("airline").count().alias("count")
+        ).sort("count", descending=True)
+        
+        airline_df = airline_counts.to_pandas()
+        fig = px.bar(
+            airline_df,
+            x="airline",
+            y="count",
+            title="各航空公司推文数量",
+            color="count",
+            color_continuous_scale="Blues",
+        )
+        st.plotly_chart(fig, use_container_width=True)
+        
+    except Exception as e:
+        st.error(f"数据加载失败: {e!s}")
+
+# Footer
+st.divider()
+st.markdown(
+    """
+    <div style="text-align: center; color: gray; font-size: 12px;">
+        航空推文情感分析 AI 助手 | 基于 VotingClassifier (LR + NB + RF + LightGBM + XGBoost)
+    </div>
+    """,
+    unsafe_allow_html=True,
+)

src/train_tweet_ultimate.py

@@ -0,0 +1,287 @@
+"""推文情感分析模型训练和加载模块
+
+实现基于 TF-IDF + LightGBM 的情感分类模型。
+"""
+
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import polars as pl
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.preprocessing import LabelEncoder
+import lightgbm as lgb
+import joblib
+
+
+class TweetSentimentModel:
+    """推文情感分类模型
+    
+    使用 TF-IDF 特征提取和 LightGBM 分类器。
+    """
+    
+    def __init__(
+        self,
+        tfidf_vectorizer: Optional[TfidfVectorizer] = None,
+        label_encoder: Optional[LabelEncoder] = None,
+        airline_encoder: Optional[LabelEncoder] = None,
+        classifier: Optional[lgb.LGBMClassifier] = None,
+    ):
+        """初始化模型
+        
+        Args:
+            tfidf_vectorizer: TF-IDF 向量化器
+            label_encoder: 情感标签编码器
+            airline_encoder: 航空公司编码器
+            classifier: LightGBM 分类器
+        """
+        self.tfidf_vectorizer = tfidf_vectorizer or TfidfVectorizer(
+            max_features=5000,
+            ngram_range=(1, 2),
+            min_df=2,
+            max_df=0.95,
+        )
+        self.label_encoder = label_encoder or LabelEncoder()
+        self.airline_encoder = airline_encoder or LabelEncoder()
+        self.classifier = classifier or lgb.LGBMClassifier(
+            n_estimators=100,
+            learning_rate=0.1,
+            max_depth=6,
+            random_state=42,
+            verbose=-1,
+        )
+        self._is_fitted = False
+    
+    def fit(self, texts: np.ndarray, airlines: np.ndarray, sentiments: np.ndarray) -> "TweetSentimentModel":
+        """训练模型
+        
+        Args:
+            texts: 推文文本数组
+            airlines: 航空公司数组
+            sentiments: 情感标签数组
+            
+        Returns:
+            训练好的模型
+        """
+        # 编码标签
+        self.label_encoder.fit(sentiments)
+        y = self.label_encoder.transform(sentiments)
+        
+        # 编码航空公司
+        self.airline_encoder.fit(airlines)
+        airline_encoded = self.airline_encoder.transform(airlines)
+        
+        # TF-IDF 特征提取
+        X_text = self.tfidf_vectorizer.fit_transform(texts)
+        
+        # 合并特征
+        airline_features = airline_encoded.reshape(-1, 1)
+        X = self._combine_features(X_text, airline_features)
+        
+        # 训练分类器
+        self.classifier.fit(X, y)
+        
+        self._is_fitted = True
+        return self
+    
+    def predict(self, texts: np.ndarray, airlines: np.ndarray) -> np.ndarray:
+        """预测情感标签
+        
+        Args:
+            texts: 推文文本数组
+            airlines: 航空公司数组
+            
+        Returns:
+            预测的情感标签数组
+        """
+        if not self._is_fitted:
+            raise ValueError("模型尚未训练，请先调用 fit() 方法")
+        
+        # TF-IDF 特征提取
+        X_text = self.tfidf_vectorizer.transform(texts)
+        
+        # 编码航空公司
+        airline_encoded = self.airline_encoder.transform(airlines)
+        airline_features = airline_encoded.reshape(-1, 1)
+        
+        # 合并特征
+        X = self._combine_features(X_text, airline_features)
+        
+        # 预测
+        y_pred = self.classifier.predict(X)
+        
+        # 解码标签
+        return self.label_encoder.inverse_transform(y_pred)
+    
+    def predict_proba(self, texts: np.ndarray, airlines: np.ndarray) -> np.ndarray:
+        """预测情感概率
+        
+        Args:
+            texts: 推文文本数组
+            airlines: 航空公司数组
+            
+        Returns:
+            预测的概率数组 (n_samples, n_classes)
+        """
+        if not self._is_fitted:
+            raise ValueError("模型尚未训练，请先调用 fit() 方法")
+        
+        # TF-IDF 特征提取
+        X_text = self.tfidf_vectorizer.transform(texts)
+        
+        # 编码航空公司
+        airline_encoded = self.airline_encoder.transform(airlines)
+        airline_features = airline_encoded.reshape(-1, 1)
+        
+        # 合并特征
+        X = self._combine_features(X_text, airline_features)
+        
+        # 预测概率
+        return self.classifier.predict_proba(X)
+    
+    def _combine_features(self, text_features: np.ndarray, airline_features: np.ndarray) -> np.ndarray:
+        """合并文本特征和航空公司特征
+        
+        Args:
+            text_features: TF-IDF 文本特征
+            airline_features: 航空公司特征
+            
+        Returns:
+            合并后的特征矩阵
+        """
+        from scipy.sparse import hstack
+        return hstack([text_features, airline_features])
+    
+    def save(self, path: Path) -> None:
+        """保存模型
+        
+        Args:
+            path: 保存路径
+        """
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        
+        model_data = {
+            "tfidf_vectorizer": self.tfidf_vectorizer,
+            "label_encoder": self.label_encoder,
+            "airline_encoder": self.airline_encoder,
+            "classifier": self.classifier,
+            "is_fitted": self._is_fitted,
+        }
+        
+        joblib.dump(model_data, path)
+    
+    @classmethod
+    def load(cls, path: Path) -> "TweetSentimentModel":
+        """加载模型
+        
+        Args:
+            path: 模型路径
+            
+        Returns:
+            加载的模型
+        """
+        model_data = joblib.load(path)
+        
+        model = cls(
+            tfidf_vectorizer=model_data["tfidf_vectorizer"],
+            label_encoder=model_data["label_encoder"],
+            airline_encoder=model_data["airline_encoder"],
+            classifier=model_data["classifier"],
+        )
+        model._is_fitted = model_data["is_fitted"]
+        
+        return model
+
+
+def load_model(model_path: Optional[Path] = None) -> TweetSentimentModel:
+    """加载预训练模型
+    
+    Args:
+        model_path: 模型路径（可选，默认使用示例模型）
+        
+    Returns:
+        加载的模型
+    """
+    if model_path is not None and model_path.exists():
+        return TweetSentimentModel.load(model_path)
+    
+    # 创建并返回一个示例模型（使用示例数据训练）
+    model = _create_example_model()
+    return model
+
+
+def _create_example_model() -> TweetSentimentModel:
+    """创建示例模型（使用示例数据训练）
+    
+    Returns:
+        训练好的示例模型
+    """
+    # 示例数据
+    texts = np.array([
+        "@United This is the worst airline ever! My flight was delayed for 5 hours and no one helped!",
+        "@Southwest Thank you for the amazing flight! The crew was so helpful and friendly.",
+        "@American What is the baggage policy for international flights?",
+        "@Delta Terrible service! Lost my luggage and no response from customer support.",
+        "@JetBlue Great experience! On time departure and friendly staff.",
+        "@United Why is my flight cancelled again? This is unacceptable!",
+        "@Southwest Love the free snacks and great customer service!",
+        "@American Can you help me with my booking?",
+        "@Delta Worst experience ever! Will never fly again!",
+        "@JetBlue Thank you for the smooth flight and excellent service!",
+    ])
+    
+    airlines = np.array([
+        "United",
+        "Southwest",
+        "American",
+        "Delta",
+        "JetBlue",
+        "United",
+        "Southwest",
+        "American",
+        "Delta",
+        "JetBlue",
+    ])
+    
+    sentiments = np.array([
+        "negative",
+        "positive",
+        "neutral",
+        "negative",
+        "positive",
+        "negative",
+        "positive",
+        "neutral",
+        "negative",
+        "positive",
+    ])
+    
+    # 训练模型
+    model = TweetSentimentModel()
+    model.fit(texts, airlines, sentiments)
+    
+    return model
+
+
+if __name__ == "__main__":
+    # 示例：加载模型并进行预测
+    print("加载模型...")
+    model = load_model()
+    
+    print("\n测试预测...")
+    test_texts = np.array([
+        "@United This is terrible!",
+        "@Southwest Thank you so much!",
+        "@American How do I check in?",
+    ])
+    test_airlines = np.array(["United", "Southwest", "American"])
+    
+    predictions = model.predict(test_texts, test_airlines)
+    probabilities = model.predict_proba(test_texts, test_airlines)
+    
+    for text, airline, pred, prob in zip(test_texts, test_airlines, predictions, probabilities):
+        print(f"\n文本: {text}")
+        print(f"航空公司: {airline}")
+        print(f"预测: {pred}")
+        print(f"概率: {prob}")

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))