1066 lines
49 KiB
Python
1066 lines
49 KiB
Python
"""
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sentiment_page.py
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=================
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Halaman Analisis Sentimen — diperbaiki untuk mengatasi semua prediksi negatif.
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PERBAIKAN vs VERSI LAMA:
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─────────────────────────────────────────────────────────────────────────────
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1. MENGGUNAKAN sentiment_service.py (HYBRID CLASSIFIER)
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Tidak lagi memanggil model secara langsung dengan predict_batch sederhana.
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Sebaliknya menggunakan sentiment_service yang sudah mengimplementasikan:
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• Lexicon-based override untuk kelas POSITIF (yang tidak ada di model)
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• Negation handling (tidak bagus ≠ bagus)
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• Fallback ke model NB untuk negatif vs netral
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2. PREPROCESSING SELARAS
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Memanggil preprocess_for_model() dari sentiment_service agar preprocessing
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yang digunakan untuk prediksi persis sama dengan yang dipakai saat training.
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3. CONFIDENCE SCORE AKURAT
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Menggunakan confidence gabungan dari lexicon score + model probability,
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bukan hanya max probability dari model yang bias ke negatif.
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4. LABEL MAPPING KONSISTEN
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Label dikembalikan selalu dalam format: 'Positif', 'Netral', 'Negatif'
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(kapital huruf pertama) agar konsisten di seluruh tampilan.
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─────────────────────────────────────────────────────────────────────────────
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"""
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import streamlit as st
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import pandas as pd
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import numpy as np
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import os
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import re
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import string
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from collections import Counter
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from datetime import datetime, timedelta
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from html import escape
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from database import engine, get_tweet_count, get_latest_crawl_time
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from timezone_utils import (
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parse_dt_with_tz,
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parse_dt_with_source_tz,
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get_timezone_label,
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get_timezone_name,
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)
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import plotly.graph_objects as go
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# ── Import sentiment_service (hybrid classifier) ──────────────────────────
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from sentiment_service import (
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preprocess_for_model,
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preprocess_untuk_lexicon,
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_hitung_skor_lexicon,
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_klasifikasi_hybrid,
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_load_stopwords,
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_load_stemmer,
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_STOPWORDS,
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_STEMMER,
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)
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from page_modules.table_utils import render_standard_table
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# ─────────────────────────────────────────────────────────────
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# Timezone & formatting helpers
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# ─────────────────────────────────────────────────────────────
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def parse_dt(series):
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return parse_dt_with_tz(series, st.session_state.get("user_timezone", "WIB (UTC+7)"))
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def parse_crawled_dt(series):
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return parse_dt_with_source_tz(
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series,
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st.session_state.get("user_timezone", "WIB (UTC+7)"),
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os.getenv("APP_TIMEZONE", "Asia/Makassar"),
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)
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def format_dt(value):
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if value is None or pd.isna(value):
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return "Belum ada"
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try:
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tz_label = get_timezone_label(st.session_state.get("user_timezone", "WIB (UTC+7)"))
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return f"{value.strftime('%d/%m/%Y %H:%M')} {tz_label}"
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except Exception:
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return "Belum ada"
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def format_now():
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now = parse_dt(pd.Series([datetime.utcnow().isoformat()])).iloc[0]
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return format_dt(now)
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def user_today():
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timezone_choice = st.session_state.get("user_timezone", "WIB (UTC+7)")
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return pd.Timestamp.now(tz=get_timezone_name(timezone_choice)).date()
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def _sync_dynamic_period():
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mode = st.session_state.get("analysis_mode")
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today = user_today()
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configs = {
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"realtime": (today - timedelta(days=6), today, "Tweet Terkini — 7 Hari Terakhir"),
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"30days": (today - timedelta(days=29), today, "30 Hari Terakhir"),
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"captured": (today, today, "Tweet Hari Ini"),
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}
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if mode not in configs:
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return
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start_day, end_day, mode_display = configs[mode]
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start_day = pd.Timestamp(start_day).date()
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end_day = pd.Timestamp(end_day).date()
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dt_start = datetime.combine(start_day, datetime.min.time())
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dt_end = datetime.combine(end_day + timedelta(days=1), datetime.min.time())
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st.session_state.filter_start_date = dt_start
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st.session_state.filter_end_date = dt_end
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st.session_state.filter_label = f"{dt_start.strftime('%d/%m/%Y')} s/d {end_day.strftime('%d/%m/%Y')}"
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st.session_state.mode_display = mode_display
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st.session_state.filter_date_column = "created_at"
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# ─────────────────────────────────────────────────────────────
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# Core prediction function (DIPERBAIKI)
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# ─────────────────────────────────────────────────────────────
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def predict_batch_hybrid(texts: list[str]) -> list[tuple[str, float]]:
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"""
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Prediksi sentimen menggunakan Hybrid Classifier dari sentiment_service.
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Pipeline per tweet:
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1. preprocess_for_model() → teks untuk TF-IDF + NB
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2. preprocess_untuk_lexicon() → teks untuk pengecekan lexicon
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3. _hitung_skor_lexicon() → hitung sinyal positif/negatif kuat
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4. _klasifikasi_hybrid() → putuskan label + confidence
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Return: list of (label, confidence)
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"""
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results = []
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for text in texts:
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teks_model = preprocess_for_model(text)
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teks_lexicon = preprocess_untuk_lexicon(text)
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teks_lower = str(text).lower()
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skor = _hitung_skor_lexicon(teks_lexicon)
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label, conf = _klasifikasi_hybrid(teks_model, skor, teks_lower)
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results.append((label, conf))
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return results
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def preprocess_single(text: str) -> str:
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"""Preprocess satu teks untuk disimpan ke kolom clean_text."""
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return preprocess_for_model(text)
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# ─────────────────────────────────────────────────────────────
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# Shared UI helpers
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# ─────────────────────────────────────────────────────────────
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def _section_header(title, subtitle=""):
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sub_html = (
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f'<div style="font-size:0.78rem;color:#64748b;margin-top:4px;line-height:1.5;">{subtitle}</div>'
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if subtitle else ""
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)
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st.markdown(
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f'<div style="background:#ffffff;border:1.5px solid #e2e8f0;border-radius:14px;'
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f'padding:1rem 1.25rem;margin-bottom:1rem;box-shadow:0 2px 6px rgba(15,23,42,0.06);">'
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f'<div style="font-size:0.95rem;font-weight:700;color:#0f172a;letter-spacing:0.02em;">{title}</div>'
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f'{sub_html}</div>',
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unsafe_allow_html=True,
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)
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def _section_gap(size="md"):
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heights = {"sm": "1rem", "md": "1.45rem", "lg": "1.9rem"}
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st.markdown(f'<div style="height:{heights.get(size,"1.45rem")};"></div>', unsafe_allow_html=True)
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def _render_sentiment_styles():
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st.markdown("""
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<style>
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@import url('https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;500;600;700;800&display=swap');
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section[data-testid="stMain"] * {
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font-family: 'Plus Jakarta Sans', sans-serif !important;
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}
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.block-container { padding-top: 1rem !important; }
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[data-testid="stMainBlockContainer"] { padding-top: 1rem !important; }
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header[data-testid="stHeader"] { height: 0 !important; min-height: 0 !important; }
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.st-key-sentiment_keyword_panel,
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.st-key-sentiment_table_controls,
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.st-key-sentiment_download_panel,
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.st-key-sentiment_chart_panel,
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.st-key-sentiment_trend_panel,
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.st-key-sentiment_wordfreq_panel {
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background: #ffffff !important;
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border: 1.5px solid #e2e8f0 !important;
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border-radius: 12px !important;
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box-shadow: 0 2px 6px rgba(15,23,42,0.06) !important;
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padding: 1.1rem 1.2rem 1rem !important;
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}
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.st-key-sentiment_keyword_panel [data-testid="stTextInput"] label p,
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.st-key-sentiment_table_controls [data-testid="stTextInput"] label p,
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.st-key-sentiment_table_controls [data-testid="stSelectbox"] label p {
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color: #334155 !important;
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font-size: 0.72rem !important;
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font-weight: 800 !important;
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letter-spacing: 0.04em !important;
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line-height: 1.2 !important;
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margin-bottom: 0.22rem !important;
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text-transform: uppercase !important;
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}
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.stButton > button {
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border-radius: 12px !important;
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font-weight: 700 !important;
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font-family: 'Plus Jakarta Sans', sans-serif !important;
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transition: all 0.18s ease !important;
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}
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.stButton > button:hover {
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transform: translateY(-1px);
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box-shadow: 0 6px 18px rgba(59,108,247,0.18) !important;
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}
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.sent-pill {
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transition: transform 0.2s cubic-bezier(.34,1.56,.64,1), box-shadow 0.2s ease;
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}
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.sent-pill:hover {
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transform: translateY(-3px);
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box-shadow: 0 10px 24px rgba(15,23,42,0.10) !important;
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}
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@keyframes fadeDown {
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from { opacity: 0; transform: translateY(-10px); }
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to { opacity: 1; transform: translateY(0); }
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}
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.sent-header { animation: fadeDown 0.45s ease both; }
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@keyframes fadeUp {
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from { opacity: 0; transform: translateY(10px); }
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to { opacity: 1; transform: translateY(0); }
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}
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.stat-1 { animation: fadeUp 0.35s 0.05s ease both; }
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.stat-2 { animation: fadeUp 0.35s 0.12s ease both; }
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.stat-3 { animation: fadeUp 0.35s 0.19s ease both; }
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.stat-4 { animation: fadeUp 0.35s 0.26s ease both; }
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.sentiment-active-context {
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display: flex; flex-wrap: wrap; gap: 0.45rem; margin-top: 0.15rem; align-items: center;
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}
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.sentiment-context-pill {
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align-items: center; background: #f8fafc; border: 1px solid #e2e8f0;
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border-radius: 999px; color: #475569; display: inline-flex;
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font-size: 0.73rem; font-weight: 750; gap: 0.35rem; line-height: 1.2;
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min-height: 28px; padding: 0.28rem 0.65rem; white-space: nowrap;
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}
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.sentiment-context-pill strong { color: #0f172a; font-weight: 850; }
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.reco-card {
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transition: transform 0.18s ease, box-shadow 0.18s ease;
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}
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.reco-card:hover {
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transform: translateX(3px);
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box-shadow: 0 6px 20px rgba(15,23,42,0.09) !important;
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}
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</style>
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""", unsafe_allow_html=True)
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# ─────────────────────────────────────────────────────────────
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# Page Header
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# ─────────────────────────────────────────────────────────────
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def _render_page_header():
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st.markdown("""
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<div class="sent-header" style="
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background: linear-gradient(135deg,#ffffff 0%,#eef2ff 50%,#ede9fe 100%);
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border: 1px solid #c7d2fe;
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border-radius: 20px;
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padding: 1.5rem 1.75rem;
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margin-bottom: 1.5rem;
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box-shadow: 0 4px 20px rgba(59,108,247,0.08);
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display: flex; align-items: center; gap: 1.1rem;
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">
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<div style="
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width:52px;height:52px;
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background:linear-gradient(135deg,#3b6cf7,#6366f1);
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border-radius:14px;
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display:flex;align-items:center;justify-content:center;
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font-size:1.5rem;
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box-shadow:0 6px 16px rgba(99,102,241,0.35);
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flex-shrink:0;
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">📈</div>
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<div>
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<h2 style="font-size:1.25rem;font-weight:800;color:#0f172a;
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margin:0 0 4px;letter-spacing:-0.01em;line-height:1.2;">
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Analisis Sentimen</h2>
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<p style="font-size:0.8rem;color:#64748b;margin:0;line-height:1.5;">
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Klasifikasi otomatis tweet menggunakan Hybrid Classifier — Positif · Netral · Negatif</p>
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</div>
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<div style="
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margin-left:auto;
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background:linear-gradient(135deg,#eef2ff,#e0e7ff);
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border:1px solid #a5b4fc;
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border-radius:10px;
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padding:0.45rem 0.9rem;
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font-size:0.72rem;font-weight:700;color:#3b6cf7;
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white-space:nowrap;letter-spacing:0.04em;text-transform:uppercase;
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">🤖 Hybrid Classifier</div>
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</div>
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""", unsafe_allow_html=True)
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# ─────────────────────────────────────────────────────────────
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# Summary Pills
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# ─────────────────────────────────────────────────────────────
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def _render_summary_pills(total, pos_n, neu_n, neg_n, pos_p, neu_p, neg_p, filter_label):
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pills = [
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("stat-1", "📊", "linear-gradient(135deg,#eef2ff,#e0e7ff)", "#3b6cf7", "#1e3a8a", "#c7d2fe",
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"Total Dianalisis", f"{total:,}", f"Periode {filter_label}"),
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("stat-2", "😊", "linear-gradient(135deg,#f0fdf4,#dcfce7)", "#16a34a", "#14532d", "#86efac",
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"Positif", f"{pos_n:,}", f"{pos_p:.1f}% dari total"),
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("stat-3", "😐", "linear-gradient(135deg,#f8fafc,#f1f5f9)", "#64748b", "#334155", "#cbd5e1",
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"Netral", f"{neu_n:,}", f"{neu_p:.1f}% dari total"),
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("stat-4", "😞", "linear-gradient(135deg,#fef2f2,#fee2e2)", "#ef4444", "#7f1d1d", "#fca5a5",
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"Negatif", f"{neg_n:,}", f"{neg_p:.1f}% dari total"),
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]
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cols = st.columns(4, gap="medium")
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for col, (anim, icon, bg, color, dark, border, label, val, sub) in zip(cols, pills):
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fs = "1.6rem" if len(str(val)) <= 6 else "1.2rem"
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with col:
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st.markdown(
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f'<div class="sent-pill {anim}" style="background:{bg};border:1.5px solid {border};'
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f'border-radius:14px;padding:1.25rem 1rem;text-align:center;'
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f'box-shadow:0 2px 8px {color}15;margin-bottom:0.5rem;">'
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f'<div style="width:40px;height:40px;background:{color};border-radius:10px;'
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f'display:flex;align-items:center;justify-content:center;font-size:1.1rem;'
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f'margin:0 auto 0.65rem;box-shadow:0 4px 10px {color}44;">{icon}</div>'
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f'<div style="font-size:0.65rem;font-weight:800;color:{color};text-transform:uppercase;'
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f'letter-spacing:0.07em;margin-bottom:0.3rem;line-height:1.3;">{label}</div>'
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f'<div style="font-size:{fs};font-weight:800;color:{dark};line-height:1.15;'
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f'margin-bottom:0.25rem;">{val}</div>'
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f'<div style="font-size:0.67rem;color:{color};font-weight:600;opacity:0.9;'
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f'line-height:1.35;">{sub}</div>'
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f'</div>',
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unsafe_allow_html=True,
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)
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# ─────────────────────────────────────────────────────────────
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# Proportion Bar
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# ─────────────────────────────────────────────────────────────
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def _render_proportion_bar(pos_p, neu_p, neg_p):
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pos_p = round(pos_p, 1)
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neu_p = round(neu_p, 1)
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neg_p = round(neg_p, 1)
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st.markdown(
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f'<div style="background:#f8fafc;border:1.5px solid #e2e8f0;border-radius:12px;'
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f'padding:1rem 1.25rem;margin-bottom:0;">'
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f'<div style="font-size:0.72rem;font-weight:800;color:#64748b;text-transform:uppercase;'
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f'letter-spacing:0.06em;margin-bottom:0.6rem;">Proporsi Sentimen Keseluruhan</div>'
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f'<div style="display:flex;border-radius:8px;overflow:hidden;height:28px;">'
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f'<div style="width:{pos_p}%;background:#16a34a;display:flex;align-items:center;'
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|
f'justify-content:center;font-size:0.68rem;font-weight:700;color:white;'
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f'white-space:nowrap;padding:0 4px;" title="Positif {pos_p}%">'
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f'{"😊 " + str(pos_p) + "%" if pos_p >= 8 else ""}</div>'
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|
f'<div style="width:{neu_p}%;background:#94a3b8;display:flex;align-items:center;'
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|
f'justify-content:center;font-size:0.68rem;font-weight:700;color:white;'
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f'white-space:nowrap;padding:0 4px;" title="Netral {neu_p}%">'
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f'{"😐 " + str(neu_p) + "%" if neu_p >= 8 else ""}</div>'
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f'<div style="width:{neg_p}%;background:#ef4444;display:flex;align-items:center;'
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f'justify-content:center;font-size:0.68rem;font-weight:700;color:white;'
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f'white-space:nowrap;padding:0 4px;" title="Negatif {neg_p}%">'
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f'{"😞 " + str(neg_p) + "%" if neg_p >= 8 else ""}</div>'
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f'</div>'
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f'<div style="display:flex;gap:1.25rem;margin-top:0.6rem;flex-wrap:wrap;">'
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f'<span style="font-size:0.7rem;color:#16a34a;font-weight:700;">● Positif {pos_p}%</span>'
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f'<span style="font-size:0.7rem;color:#94a3b8;font-weight:700;">● Netral {neu_p}%</span>'
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f'<span style="font-size:0.7rem;color:#ef4444;font-weight:700;">● Negatif {neg_p}%</span>'
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f'</div></div>',
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|
unsafe_allow_html=True,
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|
)
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# ─────────────────────────────────────────────────────────────
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# Donut + Bar chart
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|
# ─────────────────────────────────────────────────────────────
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|
|
|
def _render_donut_chart(pos_n, neu_n, neg_n, total, filter_label):
|
|
labels, values, colors = [], [], []
|
|
color_map = {"Positif": "#16a34a", "Netral": "#94a3b8", "Negatif": "#ef4444"}
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|
for label, val in [("Positif", pos_n), ("Netral", neu_n), ("Negatif", neg_n)]:
|
|
if val > 0:
|
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labels.append(label)
|
|
values.append(val)
|
|
colors.append(color_map[label])
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|
|
dominant = max([("Positif", pos_n), ("Netral", neu_n), ("Negatif", neg_n)], key=lambda x: x[1])
|
|
dom_pct = round(dominant[1] / total * 100) if total > 0 else 0
|
|
dom_emoji = {"Positif": "😊", "Netral": "😐", "Negatif": "😞"}.get(dominant[0], "📊")
|
|
|
|
fig = go.Figure(data=[go.Pie(
|
|
labels=labels, values=values, hole=0.62,
|
|
marker=dict(colors=colors, line=dict(color="white", width=4)),
|
|
textinfo="label+percent", textfont=dict(size=12),
|
|
hovertemplate="<b>%{label}</b><br>%{value:,} tweet — %{percent}<extra></extra>",
|
|
direction="clockwise", sort=False,
|
|
)])
|
|
fig.add_annotation(
|
|
text=f"<b>{dom_pct}%</b><br><span style='font-size:10px;color:#94a3b8;'>{dom_emoji} {dominant[0]}</span>",
|
|
x=0.5, y=0.5, showarrow=False, align="center", font=dict(size=18, color="#0f172a"),
|
|
)
|
|
fig.update_layout(
|
|
height=290, margin=dict(l=10, r=10, t=10, b=10),
|
|
showlegend=True,
|
|
legend=dict(orientation="h", y=-0.1, x=0.5, xanchor="center", font=dict(size=11)),
|
|
paper_bgcolor="rgba(0,0,0,0)",
|
|
)
|
|
st.plotly_chart(fig, width="stretch", config={"displayModeBar": False})
|
|
return dominant
|
|
|
|
|
|
def _render_bar_chart(pos_n, neu_n, neg_n, total):
|
|
categories = ["Positif 😊", "Netral 😐", "Negatif 😞"]
|
|
values = [pos_n, neu_n, neg_n]
|
|
colors = ["#16a34a", "#94a3b8", "#ef4444"]
|
|
percentages = [(v / total * 100) if total > 0 else 0 for v in values]
|
|
|
|
fig = go.Figure()
|
|
for cat, val, color, pct in zip(categories, values, colors, percentages):
|
|
fig.add_trace(go.Bar(
|
|
x=[cat], y=[val],
|
|
marker=dict(color=color, opacity=0.88, cornerradius=8),
|
|
text=[f"{val:,}"], textposition="outside",
|
|
textfont=dict(size=13, color="#0f172a"), width=0.5,
|
|
hovertemplate=f"<b>{cat}</b><br>{val:,} tweet ({pct:.1f}%)<extra></extra>",
|
|
))
|
|
fig.update_layout(
|
|
height=290, margin=dict(l=0, r=0, t=30, b=0),
|
|
paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
|
|
showlegend=False,
|
|
xaxis=dict(showgrid=False, tickfont=dict(size=11, color="#64748b")),
|
|
yaxis=dict(showgrid=True, gridcolor="rgba(226,232,240,0.8)", griddash="dot",
|
|
tickfont=dict(size=10, color="#94a3b8")),
|
|
)
|
|
st.plotly_chart(fig, width="stretch", config={"displayModeBar": False})
|
|
|
|
|
|
# ─────────────────────────────────────────────────────────────
|
|
# Trend chart
|
|
# ─────────────────────────────────────────────────────────────
|
|
|
|
def _render_trend_chart(fdf, filter_label, dt_start, dt_end):
|
|
_section_header(
|
|
"📅 Tren Sentimen dari Hari ke Hari",
|
|
f"Periode {filter_label} · Diupdate: {format_now()}"
|
|
)
|
|
|
|
df_tl = fdf.copy()
|
|
df_tl["date"] = pd.to_datetime(df_tl["created_at"], errors="coerce").dt.date
|
|
actual_tl = df_tl.groupby(["date", "sentiment"]).size().reset_index(name="count")
|
|
|
|
date_range = pd.date_range(pd.Timestamp(dt_start).date(), pd.Timestamp(dt_end).date(), freq="D")
|
|
base_dates = pd.DataFrame({"date": date_range.date})
|
|
|
|
fig = go.Figure()
|
|
config_lines = [
|
|
("Positif", "#16a34a", "rgba(22,163,74,0.08)"),
|
|
("Netral", "#94a3b8", "rgba(148,163,184,0.06)"),
|
|
("Negatif", "#ef4444", "rgba(239,68,68,0.08)"),
|
|
]
|
|
for sent, color, fill in config_lines:
|
|
data = base_dates.merge(
|
|
actual_tl[actual_tl["sentiment"] == sent][["date", "count"]],
|
|
on="date", how="left",
|
|
)
|
|
data["count"] = data["count"].fillna(0).astype(int)
|
|
fig.add_trace(go.Scatter(
|
|
x=data["date"], y=data["count"], name=sent,
|
|
mode="lines+markers",
|
|
line=dict(color=color, width=2.5, shape="spline", smoothing=0.8),
|
|
marker=dict(size=6, color="white", line=dict(color=color, width=2.5)),
|
|
fill="tozeroy", fillcolor=fill,
|
|
hovertemplate=f"<b>{sent}</b><br>%{{x}}: <b>%{{y}} tweet</b><extra></extra>",
|
|
))
|
|
fig.update_layout(
|
|
height=290, margin=dict(l=0, r=0, t=10, b=0),
|
|
paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
|
|
legend=dict(orientation="h", y=1.1, x=0, font=dict(size=11)),
|
|
xaxis=dict(showgrid=True, gridcolor="rgba(226,232,240,0.6)",
|
|
tickfont=dict(size=10, color="#94a3b8")),
|
|
yaxis=dict(showgrid=True, gridcolor="rgba(226,232,240,0.6)", griddash="dot",
|
|
tickfont=dict(size=10, color="#94a3b8")),
|
|
hovermode="x unified",
|
|
)
|
|
with st.container(border=True, key="sentiment_trend_panel"):
|
|
st.plotly_chart(fig, width="stretch", config={"displayModeBar": False})
|
|
|
|
|
|
# ─────────────────────────────────────────────────────────────
|
|
# Confidence histogram
|
|
# ─────────────────────────────────────────────────────────────
|
|
|
|
# def _render_confidence_chart(fdf):
|
|
# _section_header(
|
|
# "🎯 Distribusi Keyakinan Model",
|
|
# "Seberapa yakin classifier dalam mengklasifikasikan setiap tweet"
|
|
# )
|
|
|
|
# fig = go.Figure()
|
|
# sent_cfg = [
|
|
# ("Positif", "#16a34a", "rgba(22,163,74,0.7)"),
|
|
# ("Netral", "#94a3b8", "rgba(148,163,184,0.7)"),
|
|
# ("Negatif", "#ef4444", "rgba(239,68,68,0.7)"),
|
|
# ]
|
|
# for sent, color, fill_color in sent_cfg:
|
|
# sub = fdf[fdf["sentiment"] == sent]["confidence"]
|
|
# if sub.empty:
|
|
# continue
|
|
# fig.add_trace(go.Histogram(
|
|
# x=sub, name=sent, nbinsx=20,
|
|
# marker=dict(color=fill_color, line=dict(color=color, width=1)),
|
|
# opacity=0.85,
|
|
# hovertemplate=f"<b>{sent}</b><br>Keyakinan: %{{x:.0%}}<br>Jumlah: %{{y}}<extra></extra>",
|
|
# ))
|
|
# fig.update_layout(
|
|
# height=260, margin=dict(l=0, r=0, t=10, b=0), barmode="overlay",
|
|
# paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
|
|
# legend=dict(orientation="h", y=1.1, x=0, font=dict(size=11)),
|
|
# xaxis=dict(tickformat=".0%", title="Tingkat Keyakinan",
|
|
# tickfont=dict(size=10, color="#94a3b8"),
|
|
# showgrid=True, gridcolor="rgba(226,232,240,0.6)"),
|
|
# yaxis=dict(title="Jumlah Tweet",
|
|
# tickfont=dict(size=10, color="#94a3b8"),
|
|
# showgrid=True, gridcolor="rgba(226,232,240,0.6)", griddash="dot"),
|
|
# )
|
|
# with st.container(border=True, key="sentiment_conf_panel"):
|
|
# st.plotly_chart(fig, width="stretch", config={"displayModeBar": False})
|
|
|
|
|
|
# ─────────────────────────────────────────────────────────────
|
|
# Word frequency per sentimen
|
|
# ─────────────────────────────────────────────────────────────
|
|
|
|
def _render_word_freq_per_sentiment(fdf):
|
|
_section_header(
|
|
"📊 Kata Dominan per Sentimen",
|
|
"Top 15 kata paling sering muncul di masing-masing kelompok sentimen"
|
|
)
|
|
|
|
stop_extra = {"ongkos", "kirim", "gratis", "komdigi", "ongkir"}
|
|
sent_cfg = [
|
|
("Positif", "#16a34a", "#1e3a8a"),
|
|
("Netral", "#64748b", "#334155"),
|
|
("Negatif", "#ef4444", "#7f1d1d"),
|
|
]
|
|
|
|
cols = st.columns(3, gap="medium")
|
|
for col, (sent, color, dark) in zip(cols, sent_cfg):
|
|
with col:
|
|
sub = fdf[fdf["sentiment"] == sent]
|
|
all_words = " ".join(sub["clean_text"].fillna("")).split()
|
|
filtered = [w for w in all_words if len(w) > 2 and w not in stop_extra]
|
|
wf = Counter(filtered).most_common(15)
|
|
|
|
st.markdown(
|
|
f'<div style="background:#ffffff;border:1.5px solid {color}33;border-radius:14px;'
|
|
f'padding:0.9rem 1rem;box-shadow:0 2px 8px {color}10;margin-bottom:0.5rem;">'
|
|
f'<div style="display:flex;align-items:center;gap:0.5rem;margin-bottom:0.6rem;">'
|
|
f'<div style="width:30px;height:30px;background:{color};border-radius:8px;'
|
|
f'display:flex;align-items:center;justify-content:center;font-size:0.9rem;">'
|
|
f'{"😊" if sent=="Positif" else "😐" if sent=="Netral" else "😞"}</div>'
|
|
f'<div style="font-size:0.85rem;font-weight:800;color:{dark};">{sent}</div>'
|
|
f'<div style="margin-left:auto;font-size:0.68rem;color:{color};font-weight:700;">'
|
|
f'{len(sub):,} tweet</div>'
|
|
f'</div>',
|
|
unsafe_allow_html=True,
|
|
)
|
|
|
|
if not wf:
|
|
st.info("Belum ada data")
|
|
st.markdown('</div>', unsafe_allow_html=True)
|
|
continue
|
|
|
|
words_list = [w[0] for w in wf]
|
|
counts_list = [w[1] for w in wf]
|
|
max_c = max(counts_list) if counts_list else 1
|
|
|
|
fig = go.Figure(data=[go.Bar(
|
|
y=words_list[::-1], x=counts_list[::-1], orientation="h",
|
|
marker=dict(
|
|
color=[f"rgba({int(color[1:3],16)},{int(color[3:5],16)},{int(color[5:7],16)},{0.35+0.65*(c/max_c):.2f})"
|
|
for c in counts_list[::-1]],
|
|
line=dict(width=0), cornerradius=4,
|
|
),
|
|
text=[str(c) for c in counts_list[::-1]],
|
|
textposition="outside",
|
|
textfont=dict(size=9, color="#475569"),
|
|
hovertemplate="<b>%{y}</b><br>%{x} kali<extra></extra>",
|
|
)])
|
|
fig.update_layout(
|
|
height=360, margin=dict(l=0, r=40, t=4, b=4),
|
|
paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
|
|
showlegend=False,
|
|
xaxis=dict(showgrid=True, gridcolor="rgba(203,213,225,0.6)",
|
|
tickfont=dict(size=8, color="#94a3b8"), fixedrange=True),
|
|
yaxis=dict(showgrid=False, tickfont=dict(size=9, color="#334155"), fixedrange=True),
|
|
)
|
|
st.plotly_chart(fig, width="stretch", config={"displayModeBar": False})
|
|
st.markdown('</div>', unsafe_allow_html=True)
|
|
|
|
|
|
# ─────────────────────────────────────────────────────────────
|
|
# Word cloud
|
|
# ─────────────────────────────────────────────────────────────
|
|
|
|
def _render_wordcloud(fdf):
|
|
_section_header(
|
|
"☁️ Word Cloud per Sentimen",
|
|
"Kata-kata populer dari tweet yang sudah melalui preprocessing"
|
|
)
|
|
try:
|
|
from wordcloud import WordCloud
|
|
import matplotlib.pyplot as plt
|
|
|
|
stop_wc = {"ongkos", "kirim", "gratis", "komdigi", "ongkir"}
|
|
wc1, wc2, wc3 = st.columns(3, gap="medium")
|
|
cfg = [
|
|
(wc1, "Positif", "Greens", "😊 Positif", "#f0fdf4", "#14532d"),
|
|
(wc2, "Netral", "Blues", "😐 Netral", "#f8fafc", "#334155"),
|
|
(wc3, "Negatif", "Reds", "😞 Negatif", "#fef2f2", "#7f1d1d"),
|
|
]
|
|
for col, sent, cmap, title, bg, tc in cfg:
|
|
with col:
|
|
st.markdown(
|
|
f'<div style="background:{bg};border-radius:12px;padding:0.625rem 0.875rem;'
|
|
f'margin-bottom:0.5rem;text-align:center;">'
|
|
f'<span style="font-size:0.825rem;font-weight:700;color:{tc};">{title}</span></div>',
|
|
unsafe_allow_html=True,
|
|
)
|
|
sub = fdf[fdf["sentiment"] == sent]
|
|
words = [w for w in " ".join(sub["clean_text"].fillna("")).split()
|
|
if len(w) > 2 and w not in stop_wc]
|
|
if words:
|
|
wc = WordCloud(
|
|
width=420, height=260, background_color="white",
|
|
colormap=cmap, max_words=60,
|
|
relative_scaling=0.5, collocations=False,
|
|
).generate(" ".join(words))
|
|
fig, ax = plt.subplots(figsize=(5, 3.1))
|
|
ax.imshow(wc, interpolation="bilinear")
|
|
ax.axis("off")
|
|
plt.tight_layout(pad=0)
|
|
st.pyplot(fig, clear_figure=True)
|
|
else:
|
|
st.info("Data kata tidak cukup")
|
|
except ImportError:
|
|
st.warning("Install `wordcloud` dan `matplotlib` terlebih dahulu.")
|
|
|
|
|
|
# ─────────────────────────────────────────────────────────────
|
|
# Tweet table
|
|
# ─────────────────────────────────────────────────────────────
|
|
|
|
def _render_tweet_table(fdf, filter_label):
|
|
_section_header(
|
|
"📋 Tabel Analisis Sentimen",
|
|
f"Total {len(fdf):,} tweet · {filter_label}"
|
|
)
|
|
|
|
with st.container(border=True, key="sentiment_table_controls"):
|
|
tf1, tf2, tf3 = st.columns([3, 1.6, 1.7], gap="medium", vertical_alignment="bottom")
|
|
with tf1:
|
|
search = st.text_input(
|
|
"Search tweet", placeholder="Ketik kata kunci di isi tweet...",
|
|
key="sentiment_table_search",
|
|
)
|
|
with tf2:
|
|
sf = st.selectbox(
|
|
"Filter sentimen",
|
|
["Semua", "Positif 😊", "Netral 😐", "Negatif 😞"],
|
|
key="sentiment_table_filter",
|
|
)
|
|
with tf3:
|
|
sort_by = st.selectbox(
|
|
"Urutkan",
|
|
["Terbaru dulu", "Terlama dulu", "Keyakinan tertinggi"],
|
|
key="sentiment_table_sort",
|
|
)
|
|
|
|
_section_gap("sm")
|
|
|
|
tdf = fdf.copy()
|
|
if "crawled_at" not in tdf.columns:
|
|
tdf["crawled_at"] = pd.NaT
|
|
|
|
if search:
|
|
tdf = tdf[tdf["text"].str.contains(search, case=False, na=False)]
|
|
|
|
sf_map = {"Positif 😊": "Positif", "Netral 😐": "Netral", "Negatif 😞": "Negatif"}
|
|
if sf != "Semua":
|
|
tdf = tdf[tdf["sentiment"] == sf_map.get(sf, sf)]
|
|
|
|
if sort_by == "Terbaru dulu":
|
|
tdf = tdf.sort_values("created_at", ascending=False)
|
|
elif sort_by == "Terlama dulu":
|
|
tdf = tdf.sort_values("created_at", ascending=True)
|
|
else:
|
|
tdf = tdf.sort_values("confidence", ascending=False)
|
|
|
|
# out = tdf[["created_at", "crawled_at", "text", "clean_text", "sentiment", "confidence"]].copy()
|
|
out = tdf[["created_at", "crawled_at", "text", "clean_text", "sentiment"]].copy()
|
|
out["created_at"] = out["created_at"].apply(format_dt)
|
|
out["crawled_at"] = out["crawled_at"].apply(format_dt)
|
|
# out["confidence"] = out["confidence"].apply(lambda x: f"{x:.0%}")
|
|
out["sentiment"] = out["sentiment"].map({
|
|
"Positif": "😊 Positif", "Netral": "😐 Netral", "Negatif": "😞 Negatif",
|
|
}).fillna(out["sentiment"])
|
|
# out.columns = ["Tanggal Tweet", "Masuk Database", "Tweet Asli", "Tweet Bersih", "Sentimen", "Keyakinan"]
|
|
out.columns = ["Tanggal Tweet", "Masuk Database", "Tweet Asli", "Tweet Bersih", "Sentimen"]
|
|
|
|
render_standard_table(
|
|
out, height=400, min_width=1220,
|
|
badge_columns=["Sentimen"],
|
|
nowrap=["Tanggal Tweet", "Masuk Database", "Sentimen", "Keyakinan"],
|
|
wide_columns=["Tweet Asli", "Tweet Bersih"],
|
|
column_widths={
|
|
"Tanggal Tweet": "170px", "Masuk Database": "170px",
|
|
"Tweet Asli": "360px", "Tweet Bersih": "360px",
|
|
"Sentimen": "130px", "Keyakinan": "110px",
|
|
},
|
|
)
|
|
st.caption(f"Menampilkan {len(tdf):,} tweet")
|
|
return tdf
|
|
|
|
|
|
# ─────────────────────────────────────────────────────────────
|
|
# Insight & Recommendation
|
|
# ─────────────────────────────────────────────────────────────
|
|
|
|
def _render_insight_panel(dominant, pos_n, neu_n, neg_n, total, filter_label):
|
|
dom_name = dominant[0]
|
|
dom_pct = dominant[1] / total * 100 if total else 0
|
|
neg_pct = neg_n / total * 100 if total else 0
|
|
pos_pct = pos_n / total * 100 if total else 0
|
|
neu_pct = neu_n / total * 100 if total else 0
|
|
|
|
_section_header(
|
|
"💡 Insight & Rekomendasi Tindakan",
|
|
f"Berdasarkan analisis {total:,} tweet · {filter_label}"
|
|
)
|
|
|
|
dom_color = {"Positif": "#16a34a", "Netral": "#64748b", "Negatif": "#ef4444"}.get(dom_name, "#3b6cf7")
|
|
dom_bg = {"Positif": "#f0fdf4", "Netral": "#f8fafc", "Negatif": "#fef2f2"}.get(dom_name, "#eef2ff")
|
|
dom_emoji = {"Positif": "😊", "Netral": "😐", "Negatif": "😞"}.get(dom_name, "📊")
|
|
|
|
st.markdown(
|
|
f'<div style="background:{dom_bg};border:1.5px solid {dom_color}33;border-radius:14px;'
|
|
f'padding:1rem 1.25rem;margin-bottom:1rem;border-left:4px solid {dom_color};">'
|
|
f'<div style="display:flex;align-items:center;gap:0.75rem;flex-wrap:wrap;">'
|
|
f'<div style="font-size:1.75rem;">{dom_emoji}</div>'
|
|
f'<div>'
|
|
f'<div style="font-size:0.75rem;font-weight:800;color:{dom_color};text-transform:uppercase;'
|
|
f'letter-spacing:0.06em;margin-bottom:0.15rem;">Sentimen Dominan</div>'
|
|
f'<div style="font-size:1.1rem;font-weight:800;color:{dom_color};">'
|
|
f'{dom_name} · {dom_pct:.1f}%</div>'
|
|
f'</div>'
|
|
f'<div style="margin-left:auto;font-size:0.8rem;color:#64748b;">'
|
|
f'Positif: <strong>{pos_pct:.1f}%</strong> · '
|
|
f'Netral: <strong>{neu_pct:.1f}%</strong> · '
|
|
f'Negatif: <strong>{neg_pct:.1f}%</strong>'
|
|
f'</div></div></div>',
|
|
unsafe_allow_html=True,
|
|
)
|
|
|
|
rows = []
|
|
if neg_pct >= 40:
|
|
rows.append(("🔴 URGENT", "#fef2f2", "#7f1d1d", "#ef4444",
|
|
"Tanggapi Keluhan Publik",
|
|
"Sentimen negatif tinggi — ketidakpuasan signifikan",
|
|
"Buat klarifikasi resmi dan buka ruang dialog publik",
|
|
"Humas / Tim Kebijakan"))
|
|
if neg_pct >= 20:
|
|
rows.append(("🟠 TINGGI", "#fff7ed", "#7c2d12", "#ea580c",
|
|
"Tinjau Ulang Kebijakan",
|
|
f"Sentimen negatif mencapai {neg_pct:.1f}%",
|
|
"Evaluasi poin kebijakan yang paling banyak dikeluhkan",
|
|
"Tim Kebijakan"))
|
|
if neu_pct >= 30:
|
|
rows.append(("🟡 SEDANG", "#fefce8", "#713f12", "#ca8a04",
|
|
"Tingkatkan Sosialisasi",
|
|
f"Sentimen netral {neu_pct:.1f}% — publik belum berpihak",
|
|
"Perbanyak konten edukatif dan FAQ resmi",
|
|
"Tim Komunikasi"))
|
|
if pos_pct >= 20:
|
|
rows.append(("🟢 INFO", "#f0fdf4", "#14532d", "#16a34a",
|
|
"Pertahankan Momentum Positif",
|
|
f"Sentimen positif {pos_pct:.1f}%",
|
|
"Perkuat narasi positif via kanal resmi secara konsisten",
|
|
"Tim Media Sosial"))
|
|
rows.append(("🔵 RUTIN", "#eff6ff", "#1e3a8a", "#3b6cf7",
|
|
"Pemantauan Berkelanjutan",
|
|
"Opini publik dapat berubah sewaktu-waktu",
|
|
"Pantau sentimen harian dan buat laporan berkala",
|
|
"Tim Analis Data"))
|
|
|
|
df_rek = pd.DataFrame(rows, columns=["Prioritas", "bg", "tc", "bc",
|
|
"Tindakan", "Dasar Analisis",
|
|
"Rekomendasi", "Penanggung Jawab"])
|
|
|
|
for _, row in df_rek.iterrows():
|
|
st.markdown(
|
|
f'<div class="reco-card" style="background:{row.bg};border:1.5px solid {row.bc}44;'
|
|
f'border-radius:14px;padding:1rem 1.25rem;margin-bottom:0.75rem;'
|
|
f'border-left:4px solid {row.bc};">'
|
|
f'<div style="display:flex;align-items:flex-start;gap:1rem;flex-wrap:wrap;">'
|
|
f'<div style="min-width:90px;">'
|
|
f'<span style="font-size:0.72rem;font-weight:800;color:{row.bc};'
|
|
f'text-transform:uppercase;letter-spacing:0.05em;">{row.Prioritas}</span></div>'
|
|
f'<div style="flex:1;min-width:200px;">'
|
|
f'<div style="font-size:0.875rem;font-weight:700;color:{row.tc};margin-bottom:0.25rem;">'
|
|
f'{row.Tindakan}</div>'
|
|
f'<div style="font-size:0.775rem;color:{row.tc};opacity:0.75;margin-bottom:0.375rem;">'
|
|
f'📌 {row["Dasar Analisis"]}</div>'
|
|
f'<div style="font-size:0.8rem;color:{row.tc};line-height:1.6;">'
|
|
f'✅ {row.Rekomendasi}</div>'
|
|
f'</div>'
|
|
f'<div style="min-width:120px;text-align:right;">'
|
|
f'<span style="font-size:0.72rem;background:{row.bc}22;color:{row.bc};'
|
|
f'font-weight:700;padding:0.25rem 0.625rem;border-radius:20px;white-space:nowrap;">'
|
|
f'👤 {row["Penanggung Jawab"]}</span></div>'
|
|
f'</div></div>',
|
|
unsafe_allow_html=True,
|
|
)
|
|
|
|
export_df = df_rek[["Prioritas", "Tindakan", "Dasar Analisis", "Rekomendasi", "Penanggung Jawab"]]
|
|
return export_df
|
|
|
|
|
|
# ─────────────────────────────────────────────────────────────
|
|
# Main show()
|
|
# ─────────────────────────────────────────────────────────────
|
|
|
|
def show():
|
|
_render_sentiment_styles()
|
|
_render_page_header()
|
|
|
|
if "analysis_mode" not in st.session_state:
|
|
st.warning("⚠️ Silakan pilih mode tampilan di halaman Ambil Data Twitter terlebih dahulu.")
|
|
return
|
|
|
|
_sync_dynamic_period()
|
|
|
|
start_date = st.session_state.get("filter_start_date")
|
|
end_date = st.session_state.get("filter_end_date")
|
|
filter_label = st.session_state.get("filter_label", "-")
|
|
mode_display = st.session_state.get("mode_display", "-")
|
|
|
|
if start_date is None or end_date is None:
|
|
st.warning("⚠️ Silakan buka halaman Ambil Data Twitter terlebih dahulu.")
|
|
return
|
|
|
|
mode_meta = {
|
|
"realtime": ("#16a34a", "📡"),
|
|
"30days": ("#3b6cf7", "📅"),
|
|
"captured": ("#0284c7", "📆"),
|
|
"custom": ("#d97706", "🔍"),
|
|
}
|
|
mode_color, mode_icon = mode_meta.get(st.session_state.analysis_mode, ("#3b6cf7", "📊"))
|
|
|
|
st.markdown(
|
|
f'<div style="background:#fff;border-left:4px solid {mode_color};'
|
|
f'border-top:1.5px solid #e2e8f0;border-right:1.5px solid #e2e8f0;'
|
|
f'border-bottom:1.5px solid #e2e8f0;border-radius:0 12px 12px 0;'
|
|
f'padding:0.875rem 1.25rem;margin-bottom:1.5rem;'
|
|
f'box-shadow:0 2px 6px rgba(15,23,42,0.07);'
|
|
f'display:flex;align-items:center;gap:0.75rem;">'
|
|
f'<span style="font-size:1.375rem;">{mode_icon}</span>'
|
|
f'<div>'
|
|
f'<div style="font-size:0.875rem;font-weight:700;color:#0f172a;">{mode_display}</div>'
|
|
f'<div style="font-size:0.78rem;color:#475569;margin-top:2px;">'
|
|
f'Periode: <strong style="color:{mode_color};">{filter_label}</strong></div>'
|
|
f'</div></div>',
|
|
unsafe_allow_html=True,
|
|
)
|
|
|
|
# ── Load data ─────────────────────────────────────────────
|
|
try:
|
|
df_all = pd.read_sql("SELECT * FROM tweets ORDER BY created_at DESC", engine)
|
|
if df_all.empty:
|
|
st.warning("⚠️ Belum ada data tweet.")
|
|
return
|
|
df_all["created_at"] = parse_dt(df_all["created_at"])
|
|
if "crawled_at" in df_all.columns:
|
|
df_all["crawled_at"] = parse_crawled_dt(df_all["crawled_at"])
|
|
except Exception as e:
|
|
st.error(f"❌ Gagal membaca database: {e}")
|
|
return
|
|
|
|
s_dt = pd.Timestamp(start_date)
|
|
e_dt = pd.Timestamp(end_date)
|
|
df = df_all[(df_all["created_at"] >= s_dt) & (df_all["created_at"] < e_dt)].copy()
|
|
|
|
if df.empty:
|
|
st.warning(f"⚠️ Tidak ada tweet dalam periode {filter_label}.")
|
|
return
|
|
|
|
# ── Caching ───────────────────────────────────────────────
|
|
total_tweets_in_db = get_tweet_count()
|
|
latest_crawl_marker = get_latest_crawl_time() or "no-crawl"
|
|
data_marker = (total_tweets_in_db, latest_crawl_marker)
|
|
cache_key = (
|
|
f"sent_{st.session_state.analysis_mode}_"
|
|
f"{start_date}_{end_date}_{total_tweets_in_db}_{latest_crawl_marker}"
|
|
)
|
|
|
|
for old_key in list(st.session_state.keys()):
|
|
if old_key.startswith("sent_") and old_key != cache_key:
|
|
del st.session_state[old_key]
|
|
|
|
force_refresh = data_marker != st.session_state.get("_last_sentiment_data_marker")
|
|
|
|
if cache_key not in st.session_state or force_refresh:
|
|
if force_refresh:
|
|
st.info("🔄 Menyegarkan prediksi sentimen dengan data terbaru...")
|
|
with st.spinner("🔍 Preprocessing & prediksi sentimen (Hybrid Classifier)..."):
|
|
# Preprocess semua tweet
|
|
df["clean_text"] = df["text"].apply(preprocess_single)
|
|
dfc = df[df["clean_text"].str.strip().str.len() > 0].copy()
|
|
|
|
# Prediksi menggunakan hybrid classifier
|
|
results = predict_batch_hybrid(dfc["text"].tolist())
|
|
if results:
|
|
sentiments, confidences = zip(*results)
|
|
else:
|
|
sentiments, confidences = [], []
|
|
|
|
dfc["sentiment"] = list(sentiments)
|
|
dfc["confidence"] = list(confidences)
|
|
|
|
st.session_state[cache_key] = dfc
|
|
st.session_state["_last_sentiment_data_marker"] = data_marker
|
|
|
|
df_s = st.session_state[cache_key]
|
|
|
|
# ── Keyword filter ────────────────────────────────────────
|
|
_section_header("🎯 Filter Kata Kunci", "Kosongkan untuk melihat semua tweet")
|
|
|
|
with st.container(border=True, key="sentiment_keyword_panel"):
|
|
kw = st.text_input(
|
|
"Kata kunci", placeholder="Contoh: ongkir, kurir, komdigi...",
|
|
key="sentiment_keyword_search",
|
|
)
|
|
st.markdown(
|
|
f'<div class="sentiment-active-context">'
|
|
f'<span class="sentiment-context-pill" style="border-color:{mode_color}33;'
|
|
f'background:{mode_color}0f;color:{mode_color};">'
|
|
f'Mode <strong style="color:{mode_color};">{escape(str(mode_display))}</strong></span>'
|
|
f'<span class="sentiment-context-pill">'
|
|
f'Periode <strong>{escape(str(filter_label))}</strong></span>'
|
|
f'</div>',
|
|
unsafe_allow_html=True,
|
|
)
|
|
|
|
_section_gap("sm")
|
|
|
|
fdf = (
|
|
df_s[df_s["text"].str.contains(kw, case=False, na=False)].copy()
|
|
if kw else df_s.copy()
|
|
)
|
|
|
|
if fdf.empty:
|
|
st.warning("⚠️ Tidak ada tweet yang cocok dengan kata kunci tersebut.")
|
|
return
|
|
|
|
sc = fdf["sentiment"].value_counts()
|
|
total = len(fdf)
|
|
pos_n = int(sc.get("Positif", 0))
|
|
neu_n = int(sc.get("Netral", 0))
|
|
neg_n = int(sc.get("Negatif", 0))
|
|
pos_p = pos_n / total * 100
|
|
neu_p = neu_n / total * 100
|
|
neg_p = neg_n / total * 100
|
|
|
|
# ── Ringkasan ─────────────────────────────────────────────
|
|
_section_header(
|
|
"📌 Ringkasan Sentimen",
|
|
f"Berdasarkan tanggal asli tweet · {filter_label}"
|
|
)
|
|
_section_gap("sm")
|
|
_render_summary_pills(total, pos_n, neu_n, neg_n, pos_p, neu_p, neg_p, filter_label)
|
|
_section_gap("sm")
|
|
_render_proportion_bar(pos_p, neu_p, neg_p)
|
|
_section_gap("lg")
|
|
|
|
# ── Donut + Bar ───────────────────────────────────────────
|
|
col_left, col_right = st.columns(2, gap="medium")
|
|
with col_left:
|
|
_section_header("🔵 Sebaran Sentimen", f"Periode {filter_label}")
|
|
with st.container(border=True, key="sentiment_chart_panel"):
|
|
dominant = _render_donut_chart(pos_n, neu_n, neg_n, total, filter_label)
|
|
with col_right:
|
|
_section_header("📊 Perbandingan Jumlah per Sentimen", f"Periode {filter_label}")
|
|
with st.container(border=True, key="sentiment_bar_panel"):
|
|
_render_bar_chart(pos_n, neu_n, neg_n, total)
|
|
_section_gap("lg")
|
|
|
|
# ── Trend ─────────────────────────────────────────────────
|
|
_render_trend_chart(fdf, filter_label, start_date, end_date)
|
|
_section_gap("lg")
|
|
|
|
# ── Confidence ────────────────────────────────────────────
|
|
# _render_confidence_chart(fdf)
|
|
# _section_gap("lg")
|
|
|
|
# ── Word freq ─────────────────────────────────────────────
|
|
_render_word_freq_per_sentiment(fdf)
|
|
_section_gap("lg")
|
|
|
|
# ── Word cloud ────────────────────────────────────────────
|
|
_render_wordcloud(fdf)
|
|
_section_gap("lg")
|
|
|
|
# ── Tweet table ───────────────────────────────────────────
|
|
tdf = _render_tweet_table(fdf, filter_label)
|
|
_section_gap("lg")
|
|
|
|
# ── Insight ───────────────────────────────────────────────
|
|
df_rek = _render_insight_panel(dominant, pos_n, neu_n, neg_n, total, filter_label)
|
|
_section_gap("lg")
|
|
|
|
# ── Download ──────────────────────────────────────────────
|
|
_section_header("📥 Unduh Hasil Analisis")
|
|
|
|
with st.container(border=True, key="sentiment_download_panel"):
|
|
d1, d2, d3 = st.columns(3, gap="medium", vertical_alignment="bottom")
|
|
|
|
with d1:
|
|
st.download_button(
|
|
"📥 Semua Hasil Prediksi",
|
|
fdf.to_csv(index=False).encode("utf-8"),
|
|
f"hasil_prediksi_{datetime.now().strftime('%Y%m%d_%H%M')}.csv",
|
|
"text/csv", width="stretch",
|
|
)
|
|
with d2:
|
|
summary = pd.DataFrame({
|
|
"Sentimen": ["Positif", "Netral", "Negatif"],
|
|
"Jumlah": [pos_n, neu_n, neg_n],
|
|
"Persen": [f"{pos_p:.2f}%", f"{neu_p:.2f}%", f"{neg_p:.2f}%"],
|
|
})
|
|
st.download_button(
|
|
"📈 Ringkasan Sentimen",
|
|
summary.to_csv(index=False).encode("utf-8"),
|
|
f"ringkasan_{datetime.now().strftime('%Y%m%d_%H%M')}.csv",
|
|
"text/csv", width="stretch",
|
|
)
|
|
with d3:
|
|
st.download_button(
|
|
"🎯 Rekomendasi Tindakan",
|
|
df_rek.to_csv(index=False).encode("utf-8"),
|
|
f"rekomendasi_{datetime.now().strftime('%Y%m%d_%H%M')}.csv",
|
|
"text/csv", width="stretch",
|
|
) |