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| import streamlit as st | |
| import requests | |
| import pandas as pd | |
| from datetime import datetime, timedelta | |
| import nltk | |
| import numpy as np | |
| from sklearn.linear_model import LinearRegression | |
| import plotly.graph_objects as go | |
| from plotly.subplots import make_subplots | |
| import yfinance as yf | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
| # -------------------------- | |
| # CONFIG | |
| # -------------------------- | |
| st.set_page_config(page_title="📰 News Sentiment Analysis for Young Investor", layout="wide") | |
| API_KEY = "88bc396d4eab4be494a4b86ec842db47" | |
| # -------------------------- | |
| # โหลด FinBERT model | |
| # -------------------------- | |
| def load_finbert(): | |
| tokenizer = AutoTokenizer.from_pretrained("project-aps/finbert-finetune") | |
| model = AutoModelForSequenceClassification.from_pretrained("project-aps/finbert-finetune") | |
| return tokenizer, model | |
| tokenizer, model = load_finbert() | |
| # -------------------------- | |
| # โหลด Zero-shot classifier สำหรับธีมข่าว | |
| # -------------------------- | |
| def load_theme_classifier(): | |
| return pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| theme_classifier = load_theme_classifier() | |
| candidate_labels = ["Stock Movement", "Earnings", "M&A", "Regulation", "Product Launch", "Market Analysis"] | |
| # -------------------------- | |
| # UTILITIES | |
| # -------------------------- | |
| def analyze_text(text): | |
| """วิเคราะห์อารมณ์ของข่าวด้วย FinBERT""" | |
| if not text or not text.strip(): | |
| return 0 | |
| inputs = tokenizer( | |
| text, | |
| return_tensors="pt", | |
| padding=True, | |
| truncation=True, | |
| max_length=512 | |
| ) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| probs = torch.softmax(logits, dim=1).numpy()[0] | |
| # FinBERT = [negative, neutral, positive] | |
| score = (-1 * probs[0]) + (0 * probs[1]) + (1 * probs[2]) | |
| return float(score) | |
| def summarize_themes(news_texts): | |
| """สรุปธีมข่าวด้วย Zero-shot classification""" | |
| themes = [] | |
| for text in news_texts: | |
| if not text.strip(): | |
| continue | |
| result = theme_classifier(text, candidate_labels) | |
| themes.append(result["labels"][0]) | |
| return themes | |
| # -------------------------- | |
| # แปลงชื่อ/ตัวย่อ → (Company Name, Symbol) | |
| # -------------------------- | |
| def resolve_company_symbol(keyword: str): | |
| keyword = keyword.strip() | |
| ticker = None | |
| name = None | |
| try: | |
| data = yf.Ticker(keyword) | |
| info = data.info | |
| if "symbol" in info and info["symbol"]: | |
| ticker = info["symbol"] | |
| name = info.get("longName", info.get("shortName", keyword)) | |
| else: | |
| url = f"https://query2.finance.yahoo.com/v1/finance/search?q={keyword}" | |
| res = requests.get(url).json() | |
| if "quotes" in res and len(res["quotes"]) > 0: | |
| q = res["quotes"][0] | |
| ticker = q.get("symbol") | |
| name = q.get("longname", q.get("shortname", keyword)) | |
| except: | |
| pass | |
| if not ticker: | |
| ticker = keyword.upper() | |
| if not name: | |
| name = keyword.capitalize() | |
| return name, ticker | |
| # -------------------------- | |
| # ดึงข่าว 7 วัน | |
| # -------------------------- | |
| def fetch_financial_news(keyword): | |
| company, symbol = resolve_company_symbol(keyword) | |
| to_date = datetime.now().strftime('%Y-%m-%d') | |
| from_date = (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d') | |
| query_keyword = f"({company} OR {symbol}) finance stock" | |
| all_articles = [] | |
| page = 1 | |
| while True: | |
| url = ( | |
| f"https://newsapi.org/v2/everything?" | |
| f"q={query_keyword}&" | |
| f"from={from_date}&to={to_date}&" | |
| f"language=en&sortBy=publishedAt&" | |
| f"pageSize=100&page={page}&apiKey={API_KEY}" | |
| ) | |
| r = requests.get(url) | |
| data = r.json() | |
| if data.get("status") != "ok": | |
| st.error(f"API Error: {data}") | |
| break | |
| articles = data.get("articles", []) | |
| if not articles: | |
| break | |
| for a in articles: | |
| if a["description"]: | |
| all_articles.append({ | |
| "date": pd.to_datetime(a["publishedAt"]), | |
| "text": f"{a['title']} {a['description']}", | |
| "source": a["source"]["name"], | |
| "url": a["url"] | |
| }) | |
| if len(articles) < 100: | |
| break | |
| page += 1 | |
| return pd.DataFrame(all_articles) | |
| # -------------------------- | |
| # ดึงราคาหุ้น | |
| # -------------------------- | |
| def fetch_stock_price(symbol, start_date, end_date): | |
| try: | |
| start_str = (start_date - timedelta(days=2)).strftime('%Y-%m-%d') | |
| end_str = (end_date + timedelta(days=1)).strftime('%Y-%m-%d') | |
| df = yf.download(symbol, start=start_str, end=end_str, interval="1d") | |
| if df.empty: | |
| st.warning("ไม่พบข้อมูลราคาหุ้น") | |
| return pd.DataFrame() | |
| df = df.reset_index() | |
| df_subset = df[['Date', 'Close']] | |
| df_subset.columns = ['date', 'price'] | |
| df_subset["date"] = pd.to_datetime(df_subset["date"].dt.date) | |
| return df_subset | |
| except Exception as e: | |
| st.warning(f"ดึงราคาหุ้นล้มเหลว: {e}") | |
| return pd.DataFrame() | |
| # -------------------------- | |
| # MAIN APP | |
| # -------------------------- | |
| def main(): | |
| st.title("📰 News Sentiment Analysis for Young Investor") | |
| st.markdown("วิเคราะห์แนวโน้มอารมณ์ของข่าวย้อนหลัง 7 วัน พร้อมราคาหุ้น") | |
| # Sidebar | |
| with st.sidebar: | |
| keyword = st.text_input("ค้นหา Stock Symbol (เช่น AAPL, TSLA):", "") | |
| analyze_btn = st.button("วิเคราะห์เลย") | |
| if not analyze_btn: | |
| st.info("กรอกคำค้นแล้วกด 'วิเคราะห์เลย'") | |
| return | |
| # ดึงข่าว | |
| st.info(f"กำลังดึงข่าวย้อนหลัง 7 วันสำหรับ '{keyword}'...") | |
| news_df = fetch_financial_news(keyword) | |
| if news_df.empty: | |
| st.warning("ไม่พบบทความข่าว") | |
| return | |
| # วิเคราะห์ Sentiment | |
| st.info("กำลังวิเคราะห์อารมณ์ของข่าว...") | |
| news_df["sentiment"] = news_df["text"].apply(analyze_text) | |
| news_df["date"] = pd.to_datetime(news_df["date"]) | |
| # Metrics | |
| avg_sentiment = news_df["sentiment"].mean() | |
| pos_pct = (news_df["sentiment"] > 0.1).mean() * 100 | |
| neg_pct = (news_df["sentiment"] < -0.1).mean() * 100 | |
| col1, col2, col3 = st.columns(3) | |
| col1.metric("ค่าเฉลี่ยอารมณ์ข่าว", f"{avg_sentiment:.2f}") | |
| col2.metric("ข่าวเชิงบวก", f"{pos_pct:.1f}%") | |
| col3.metric("ข่าวเชิงลบ", f"{neg_pct:.1f}%") | |
| # --------------------------------------------------------- | |
| # ธีมข่าวแทน Word Cloud | |
| # --------------------------------------------------------- | |
| st.subheader("📰 ธีมข่าว (Top Theme per Article)") | |
| news_df["theme"] = summarize_themes(news_df["text"].tolist()) | |
| theme_counts = news_df["theme"].value_counts() | |
| st.bar_chart(theme_counts) | |
| # --------------------------------------------------------- | |
| # ส่วนกราฟ Sentiment & Price (เหมือนเดิม) | |
| # --------------------------------------------------------- | |
| st.subheader("📈 แนวโน้มอารมณ์ของข่าว & ราคาหุ้น") | |
| news_df["date_day"] = pd.to_datetime(news_df["date"].dt.date) | |
| def sentiment_type(score): | |
| if score > 0.1: | |
| return "positive" | |
| if score < -0.1: | |
| return "negative" | |
| return "neutral" | |
| news_df["sentiment_type"] = news_df["sentiment"].apply(sentiment_type) | |
| daily_avg = news_df.groupby("date_day")["sentiment"].mean().reset_index(name="avg_sentiment") | |
| daily_counts = news_df.groupby(["date_day", "sentiment_type"]).size().unstack(fill_value=0).reset_index() | |
| df_sorted = pd.merge(daily_avg, daily_counts, on="date_day").sort_values("date_day") | |
| if len(df_sorted) < 2: | |
| st.warning("ข้อมูลไม่พอสร้างแนวโน้ม") | |
| st.dataframe(news_df) | |
| return | |
| # ดึงราคาหุ้น | |
| _, symbol = resolve_company_symbol(keyword) | |
| min_date, max_date = df_sorted["date_day"].min(), df_sorted["date_day"].max() | |
| st.info(f"กำลังดึงราคาหุ้น {symbol} ...") | |
| stock_df = fetch_stock_price(symbol, min_date, max_date) | |
| plot_data = pd.merge(df_sorted, stock_df, left_on="date_day", right_on="date", how="left") | |
| # Correlation | |
| correlation = plot_data['price'].corr(plot_data['avg_sentiment']) | |
| corr_text = "ไม่มีความสัมพันธ์" | |
| if correlation > 0.5: | |
| corr_text = "มีความสัมพันธ์ในทิศทางเดียวกัน" | |
| elif correlation < -0.5: | |
| corr_text = "มีความสัมพันธ์ในทิศทางตรงข้าม" | |
| st.metric("วิเคราะห์ความสัมพันธ์ระหว่างอารมณ์ของข่าวกับราคาหุ้น (Correlation)", corr_text, f"{correlation:.2f}") | |
| # Forecast Sentiment | |
| plot_data["timestamp"] = (plot_data["date_day"] - plot_data["date_day"].min()).dt.days | |
| train_data = plot_data.dropna(subset=['avg_sentiment']) | |
| if len(train_data) >= 2: | |
| model_lr = LinearRegression() | |
| model_lr.fit(train_data[["timestamp"]], train_data["avg_sentiment"]) | |
| future_days = 7 | |
| future_timestamps = np.arange( | |
| plot_data["timestamp"].max() + 1, | |
| plot_data["timestamp"].max() + future_days + 1 | |
| ) | |
| future_dates = [plot_data["date_day"].max() + timedelta(days=i) for i in range(1, future_days + 1)] | |
| future_preds = model_lr.predict(future_timestamps.reshape(-1, 1)) | |
| # Plot | |
| fig = make_subplots(rows=2, cols=1, specs=[[{"secondary_y": True}], [{}]], row_heights=[0.7, 0.3], vertical_spacing=0.1, shared_xaxes=True) | |
| # ราคาหุ้น | |
| fig.add_trace(go.Scatter(x=plot_data["date_day"], y=plot_data["price"], name=f"{symbol} Price", mode="lines+markers", line=dict(color="orange")), row=1, col=1, secondary_y=False) | |
| # Sentiment จริง | |
| fig.add_trace(go.Scatter(x=plot_data["date_day"], y=plot_data["avg_sentiment"], name="Actual Sentiment", mode="lines+markers", line=dict(color="blue")), row=1, col=1, secondary_y=True) | |
| # Sentiment พยากรณ์ | |
| if "future_preds" in locals(): | |
| fig.add_trace(go.Scatter(x=future_dates, y=future_preds, name="Predicted Sentiment", mode="lines+markers", line=dict(color="#05a0fa", dash="dash")), row=1, col=1, secondary_y=True) | |
| # เส้นเชื่อม Actual -> Predicted | |
| last_actual_date = plot_data["date_day"].max() | |
| last_actual_value = plot_data["avg_sentiment"].iloc[-1] | |
| first_pred_date = future_dates[0] | |
| first_pred_value = future_preds[0] | |
| fig.add_trace(go.Scatter(x=[last_actual_date, first_pred_date], y=[last_actual_value, first_pred_value], mode="lines", line=dict(color="#05a0fa", dash="dot"), name="Connector Actual→Predicted"), row=1, col=1, secondary_y=True) | |
| # จำนวนข่าว | |
| for col in ["neutral", "negative", "positive"]: | |
| if col not in plot_data.columns: | |
| plot_data[col] = 0 | |
| fig.add_trace(go.Bar(x=plot_data["date_day"], y=plot_data["neutral"], name="Neutral", marker_color='rgba(128, 128, 128, 0.7)'), row=2, col=1) | |
| fig.add_trace(go.Bar(x=plot_data["date_day"], y=plot_data["negative"], name="Negative", marker_color='rgba(255, 0, 0, 0.7)'), row=2, col=1) | |
| fig.add_trace(go.Bar(x=plot_data["date_day"], y=plot_data["positive"], name="Positive", marker_color='rgba(0, 128, 0, 0.7)'), row=2, col=1) | |
| fig.update_layout(title=f"แนวโน้มอารมณ์ของข่าว + ราคาหุ้น ({symbol})", barmode="stack", height=650, hovermode="x unified", template="plotly_white") | |
| st.plotly_chart(fig, use_container_width=True) | |
| # แสดงรายการข่าว | |
| st.subheader("📰 รายการข่าวทั้งหมด") | |
| st.dataframe(news_df[["date", "source", "text", "sentiment", "theme", "url"]], use_container_width=True) | |
| # --------------------------------------------------------- | |
| # RUN APP | |
| # --------------------------------------------------------- | |
| if __name__ == "__main__": | |
| nltk.download("stopwords", quiet=True) | |
| main() |