Clean commit ohne Tokens

analyze_aspects.py

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+#!/usr/bin/env python3
+# analyze_aspects.py
+
+#python /Users/fischer/Desktop/HanserMVP/scraping/analyze_aspects.py --isbn "9783446264199" --db-path /Users/fischer/Desktop/buch_datenbank.sqlite --languages de
+# python analyze_aspects.py --isbn "9783446264199" --db-path /Pfad/zur/sqlite.db --languages de
+
+import sqlite3
+import argparse
+import logging
+from pathlib import Path
+import nltk
+from transformers import pipeline
+from collections import defaultdict
+import matplotlib.pyplot as plt
+
+
+def visualize_aspects(aspect_results: dict[str, list[float]], output_dir: Path, filename: str = "sentiment_aspekte.png"):
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    aspects = list(aspect_results.keys())
+    avg_scores = [sum(scores) / len(scores) for scores in aspect_results.values()]
+    colors = ['green' if score > 0.1 else 'red' if score < -0.1 else 'gray' for score in avg_scores]
+
+    plt.figure(figsize=(10, 6))
+    bars = plt.barh(aspects, avg_scores, color=colors)
+    plt.axvline(x=0, color='black', linewidth=0.8)
+    plt.xlabel("Durchschnittlicher Sentiment-Score")
+    plt.title("Sentiment-Analyse pro Aspekt")
+
+    for bar, score in zip(bars, avg_scores):
+        plt.text(bar.get_width() + 0.01, bar.get_y() + bar.get_height() / 2,
+                 f"{score:.2f}", va='center')
+
+    plt.tight_layout()
+    plt.gca().invert_yaxis()
+
+    output_path = output_dir / filename
+    plt.savefig(output_path, dpi=300)
+    plt.close()
+
+    logger.info(f"Diagramm gespeichert unter: {output_path}")
+
+
+# NLTK punkt model for sentence tokenization
+nltk.download('punkt')
+from nltk import sent_tokenize
+
+# Logging Configuration
+def configure_logging():
+    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+    return logging.getLogger(__name__)
+
+logger = configure_logging()
+
+# Aspekt-Label-Maps
+ASPECT_LABEL_MAP = {
+    "Handlung": ["Handlung", "Plot", "Story", "Aufbau"],
+    "Charaktere": ["Charaktere", "Figuren", "Protagonisten", "Nebenfiguren", "Beziehungen"],
+    "Stil": ["Stil", "Sprachstil", "Sprache", "Erzählweise"],
+    "Emotionale Wirkung": ["Lesevergnügen", "Berührend", "Bewegend", "Begeisternd", "Spannend"],
+    "Tiefgang": ["Tiefgang", "Nachdenklich", "Philosophisch", "kritisch"],
+    "Thema & Kontext": ["Thema", "Motiv", "Zeitgeschehen", "Historischer Kontext", "Gesellschaft"],
+    "Originalität": ["Originalität", "Kreativität", "Innovativ", "Idee", 'Humor'],
+    "Recherche & Authentizität": ["Recherche", "Authentizität", "Realismus", "Fakten"]
+}
+
+ASPECT_LABEL_MAP_EN = {
+    "Plot": ["Plot", "Story", "Narrative", "Structure"],
+    "Characters": ["Characters", "Protagonists", "Antagonists", "Relationships"],
+    "Style": ["Style", "Language", "Tone", "Narration"],
+    "Emotional Impact": ["Touching", "Funny", "Exciting", "Moving", "Engaging"],
+    "Depth": ["Philosophical", "Thought-provoking", "Insightful", "Critical"],
+    "Theme & Context": ["Theme", "Motif", "Historical Context", "Social Issues"],
+    "Originality": ["Originality", "Creativity", "Innovation", "Idea"],
+    "Research & Authenticity": ["Research", "Authenticity", "Realism", "Facts"]
+}
+
+ALL_LABELS = [label for labels in ASPECT_LABEL_MAP.values() for label in labels]
+
+
+# --- Datenbankzugriff ---
+
+def load_reviews(db_path: Path, isbn: str) -> list:
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+    cursor.execute(
+        "SELECT id, cleaned_text, cleaned_text_en FROM reviews_und_notizen WHERE buch_isbn = ?",
+        (isbn,)
+    )
+    rows = cursor.fetchall()
+    conn.close()
+    texts_to_analyze = []
+    for review_id, text_de, text_en in rows:
+        if text_de and isinstance(text_de, str):
+            texts_to_analyze.append((review_id, text_de, 'de'))
+        if text_en and isinstance(text_en, str):
+            texts_to_analyze.append((review_id, text_en, 'en'))
+    return texts_to_analyze
+
+
+# --- Analysefunktion ---
+
+def analyze_quickwin(db_path: Path, isbn: str, device: int = -1, languages: list[str] = ["de", "en"]) -> dict:
+    reviews = load_reviews(db_path, isbn)
+    reviews = [r for r in reviews if r[2] in languages]
+    if not reviews:
+        logger.warning(f"Keine gesäuberten Reviews für ISBN {isbn} in den gewählten Sprachen gefunden.")
+        return {}
+
+    zsl = pipeline("zero-shot-classification", model="facebook/bart-large-mnli", device=device, multi_label=True)
+    sent_de = pipeline("sentiment-analysis", model="oliverguhr/german-sentiment-bert", device=device)
+    sent_en = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english", device=device)
+
+    aspect_results = defaultdict(list)
+    total_aspects = 0
+
+    for review_id, text, lang in reviews:
+        if not text:
+            continue
+
+        logger.info(f"Review ID {review_id} ({lang}) wird verarbeitet.")
+        sentences = sent_tokenize(text, language='german' if lang == 'de' else 'english')
+
+        if lang == 'de':
+            aspect_map = ASPECT_LABEL_MAP
+            all_labels = ALL_LABELS
+            sent_pipeline = sent_de
+            hypothesis_template = "Dieser Satz handelt von {}."
+        elif lang == 'en':
+            aspect_map = ASPECT_LABEL_MAP_EN
+            all_labels = [label for labels in aspect_map.values() for label in labels]
+            sent_pipeline = sent_en
+            hypothesis_template = "This sentence is about {}."
+        else:
+            continue
+
+        for sent in sentences:
+            if not sent.strip() or len(sent) < 15:
+                continue
+
+            result = zsl(sent, candidate_labels=all_labels, hypothesis_template=hypothesis_template)
+
+            main_label = ""
+            best_score = 0.0
+            for label, score in zip(result["labels"], result["scores"]):
+                if score > 0.8:
+                    main_label = next((k for k, v in aspect_map.items() if label in v), label)
+                    best_score = score
+                    break
+
+            if not main_label:
+                continue
+
+            ml_sentiment = sent_pipeline(sent)[0]
+            ml_score = ml_sentiment['score'] if ml_sentiment['label'].upper().startswith('POS') else -ml_sentiment['score']
+            final_score = ml_score
+            final_label = 'POS' if final_score > 0.1 else 'NEG' if final_score < -0.1 else 'NEU'
+
+            print(
+                f"Review {review_id} ({lang}) | Satz: {sent}\n"
+                f"  Aspekt: {main_label} (via '{result['labels'][0]}', {best_score:.2f}) | "
+                f"ML: {ml_sentiment['label']}({ml_sentiment['score']:.2f}) -> Final: {final_label}({final_score:.2f})"
+            )
+
+            aspect_results[main_label].append(final_score)
+            total_aspects += 1
+
+    logger.info(f"Total aspects found: {total_aspects}")
+    return aspect_results
+
+
+# --- Entry Point ---
+
+def main():
+    parser = argparse.ArgumentParser(description="Quick-Win ABSA ohne SentiWS")
+    parser.add_argument("--db-path", required=True, help="Pfad zur SQLite-Datenbank")
+    parser.add_argument("--isbn", required=True, help="ISBN des Buchs")
+    parser.add_argument("--gpu", action="store_true", help="GPU verwenden (device=0)")
+    parser.add_argument("--languages", nargs="+", choices=["de", "en"], default=["de", "en"],
+                        help="Sprachen der Reviews, z. B. --languages de oder --languages de en")
+    args = parser.parse_args()
+
+    device = 0 if args.gpu else -1
+    aspect_results = analyze_quickwin(
+        Path(args.db_path), args.isbn,
+        device=device,
+        languages=args.languages
+    )
+
+    if aspect_results:
+        output_dir = Path("output")
+        visualize_aspects(aspect_results, output_dir)
+    else:
+        logger.info("Keine Aspekt-Daten zur Visualisierung verfügbar.")

aspect-sentiment-analyzer/.gitattributes

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aspect-sentiment-analyzer/README.md

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+---
+title: Aspect Sentiment Analyzer
+emoji: 🌖
+colorFrom: yellow
+colorTo: pink
+sdk: gradio
+sdk_version: 5.34.2
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference