Create Liz Mini Project.docx

idea/鬼滅の刃.py

@@ -0,0 +1,298 @@
+
+
+
+# In[247]:
+
+
+# Characters to track (Tanjirou, Zenitsu, Inosuke)
+main_characters = {
+    "Tanjirou Kamado": "Tanjirou",
+    "Zenitsu Agatsuma": "Zenitsu",
+    "Inosuke Hashibira": "Inosuke"
+}
+
+
+# In[248]:
+
+
+def analyze_sentiment(character, season):
+    """
+    Performs sentiment analysis on a character's dialogue in a given season.
+    Returns polarity (-1 to 1) and subjectivity (0 to 1).
+    """
+    dialogue = season_dialogue_data.get(season, {}).get(character, [])
+    text = ' '.join(dialogue)
+
+    if not text.strip():
+        return None  # No dialogue for this character
+
+    blob = TextBlob(text)
+    polarity = blob.sentiment.polarity
+    subjectivity = blob.sentiment.subjectivity
+
+    return {"polarity": polarity, "subjectivity": subjectivity}
+
+
+# In[250]:
+
+
+# Analyze sentiment for each character per season
+sentiment_data = {}
+
+for season in season_dialogue_data.keys():
+    sentiment_data[season] = {}
+    for full_name, short_name in tracked_characters.items():
+        sentiment_data[season][full_name] = analyze_sentiment(short_name, season)
+
+# Display sentiment analysis results
+for season, characters in sentiment_data.items():
+    print(f"\n Sentiment Analysis for {season}:")
+    for character, sentiment in characters.items():
+        if sentiment:
+            print(f"  - {character}: Polarity = {sentiment['polarity']:.2f}, Subjectivity = {sentiment['subjectivity']:.2f}")
+
+
+# In[251]:
+
+
+# Prepare sentiment analysis data for visualization
+sentiment_records = []
+
+for season, characters in sentiment_data.items():
+    for character, sentiment in characters.items():
+        if sentiment:
+            sentiment_records.append({
+                "Character": character,
+                "Season": season,
+                "Polarity": sentiment["polarity"],
+                "Subjectivity": sentiment["subjectivity"]
+            })
+
+# Convert list of dictionaries to a DataFrame
+sentiment_df = pd.DataFrame(sentiment_records)
+
+
+# In[255]:
+
+
+# Convert Season to categorical type for proper ordering
+sentiment_df["Season"] = pd.Categorical(sentiment_df["Season"], categories=sorted(season_dialogue_data.keys()), ordered=True)
+
+# Plot sentiment polarity trends across seasons for each character
+plt.figure(figsize=(12, 6))
+for character in tracked_characters.keys():
+    character_data = sentiment_df[sentiment_df["Character"] == character]
+    if not character_data.empty:
+        plt.plot(character_data["Season"], character_data["Polarity"], marker='o', label=character)
+
+plt.xlabel("Season")
+plt.ylabel("Sentiment Polarity")
+plt.title("Sentiment Polarity Trends Across Seasons")
+plt.xticks(rotation=45)
+plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
+plt.grid(True)
+plt.show()
+
+
+# In[253]:
+
+
+# Identify most positive and most negative character
+avg_polarity = sentiment_df.groupby("Character")["Polarity"].mean()
+most_positive_character = avg_polarity.idxmax()
+most_negative_character = avg_polarity.idxmin()
+
+print(f"\n Most Positive Character Overall: {most_positive_character} (Avg Polarity: {avg_polarity.max():.2f})")
+print(f" Most Negative Character Overall: {most_negative_character} (Avg Polarity: {avg_polarity.min():.2f})")
+
+
+# In[220]:
+
+
+nlp = spacy.load("en_core_web_lg")
+
+def lexical_diversity(character, season):
+    """
+    Measures lexical diversity (unique words / total words) for a character in a season.
+    Aggregates dialogue from all episodes in that season.
+    """
+    season_dialogue = []
+
+    # Iterate through all episodes, making sure they belong to the season
+    for episode, episode_data in dialogue_data.items():
+        if season in episode:  # Check if the episode belongs to the given season
+            season_dialogue.extend(episode_data.get(character, []))
+
+    # Join all dialogue into a single string
+    text = ' '.join(season_dialogue)
+
+    # If there is no dialogue, return 0 instead of None
+    if not text.strip():
+        return 0
+
+    # Process text with spaCy for tokenization
+    doc = nlp(text)
+    words = [token.text.lower() for token in doc if token.is_alpha]
+
+    # Compute lexical diversity
+    unique_words = set(words)
+    diversity_score = len(unique_words) / len(words) if words else 0
+
+    return diversity_score
+
+
+# In[258]:
+
+
+diversity_tanjirou = lexical_diversity("Tanjirou", "Demon Slayer S.1")
+diversity_zenitsu = lexical_diversity("Zenitsu", "Demon Slayer S.1")
+diversity_inosuke = lexical_diversity("Inosuke", "Demon Slayer S.1")
+
+print(f"Tanjirou's Lexical Diversity in Season 1: {diversity_tanjirou}")
+print(f"Zenitsu's Lexical Diversity in Season 1: {diversity_zenitsu}")
+print(f"Inosuke's Lexical Diversity in Season 1: {diversity_inosuke}")
+
+
+# In[259]:
+
+
+diversity_tanjirou = lexical_diversity("Tanjirou", "Demon Slayer S.2")
+diversity_zenitsu = lexical_diversity("Zenitsu", "Demon Slayer S.2")
+diversity_inosuke = lexical_diversity("Inosuke", "Demon Slayer S.2")
+
+print(f"Tanjirou's Lexical Diversity in Season 2: {diversity_tanjirou}")
+print(f"Zenitsu's Lexical Diversity in Season 2: {diversity_zenitsu}")
+print(f"Inosuke's Lexical Diversity in Season 2: {diversity_inosuke}")
+
+
+# In[257]:
+
+
+diversity_tanjirou = lexical_diversity("Tanjirou", "Demon Slayer S.3")
+diversity_zenitsu = lexical_diversity("Zenitsu", "Demon Slayer S.3")
+diversity_inosuke = lexical_diversity("Inosuke", "Demon Slayer S.3")
+
+print(f"Tanjirou's Lexical Diversity in Season 3: {diversity_tanjirou}")
+print(f"Zenitsu's Lexical Diversity in Season 3: {diversity_zenitsu}")
+print(f"Inosuke's Lexical Diversity in Season 3: {diversity_inosuke}")
+
+
+# In[256]:
+
+
+diversity_tanjirou = lexical_diversity("Tanjirou", "Demon Slayer S.4")
+diversity_zenitsu = lexical_diversity("Zenitsu", "Demon Slayer S.4")
+diversity_inosuke = lexical_diversity("Inosuke", "Demon Slayer S.4")
+
+print(f"Tanjirou's Lexical Diversity in Season 4: {diversity_tanjirou}")
+print(f"Zenitsu's Lexical Diversity in Season 4: {diversity_zenitsu}")
+print(f"Inosuke's Lexical Diversity in Season 4: {diversity_inosuke}")
+
+
+# In[240]:
+
+
+def extract_episode_number(episode_name):
+    """
+    Extracts episode number from an episode string.
+    Example: "Demon Slayer S.4 E.03" -> 3
+    """
+    match = re.search(r"E\.(\d+)", episode_name)
+    return int(match.group(1)) if match else float('inf')  # Handle unexpected cases
+
+
+# In[261]:
+
+
+def analyze_sentiment_with_events(character, season):
+    """
+    Computes sentiment analysis over time and overlays key events.
+    Ensures episodes are sorted numerically.
+    """
+    sentiment_records = []
+
+    # Iterate through all episodes in the given season
+    for episode, episode_data in dialogue_data.items():
+        if season in episode:
+            dialogues = episode_data.get(character, [])
+            episode_text = ' '.join(dialogues)
+
+            if episode_text.strip():
+                sentiment = TextBlob(episode_text).sentiment
+                sentiment_records.append({
+                    "Episode": episode,
+                    "Episode_Number": extract_episode_number(episode),
+                    "Polarity": sentiment.polarity
+                })
+
+    # Convert to DataFrame and sort by episode number
+    sentiment_df = pd.DataFrame(sentiment_records)
+    sentiment_df = sentiment_df.sort_values(by="Episode_Number")  # Sort numerically
+
+    # Plot sentiment trend over episodes
+    if not sentiment_df.empty:
+        plt.figure(figsize=(10, 5))
+        plt.plot(sentiment_df["Episode"], sentiment_df["Polarity"], marker='o', linestyle='-', label="Polarity")
+        plt.xticks(rotation=45, ha='right')
+        plt.axhline(y=0, color='gray', linestyle='--')
+
+        plt.title(f"Sentiment Analysis for {character} in {season}")
+        plt.xlabel("Episode")
+        plt.ylabel("Sentiment Polarity")
+        plt.legend()
+        plt.grid()
+        plt.show()
+
+analyze_sentiment_with_events("Inosuke", "Demon Slayer S.4")
+
+
+# In[246]:
+
+
+def compare_character_sentiment(season, characters):
+    """
+    Compares sentiment trends of multiple characters over a season.
+    Ensures episodes are sorted numerically.
+    """
+    plt.figure(figsize=(12, 6))
+
+    for character in characters:
+        sentiment_records = []
+
+        for episode, episode_data in dialogue_data.items():
+            if season in episode:
+                dialogues = episode_data.get(character, [])
+                episode_text = ' '.join(dialogues)
+
+                if episode_text.strip():
+                    sentiment = TextBlob(episode_text).sentiment
+                    sentiment_records.append({
+                        "Episode": episode,
+                        "Episode_Number": extract_episode_number(episode),
+                        "Polarity": sentiment.polarity
+                    })
+
+        # Convert to DataFrame and sort by episode number
+        sentiment_df = pd.DataFrame(sentiment_records)
+        sentiment_df = sentiment_df.sort_values(by="Episode_Number")  # Ensure correct order
+
+        if not sentiment_df.empty:
+            plt.plot(sentiment_df["Episode"], sentiment_df["Polarity"], marker='o', linestyle='-', label=character)
+
+    plt.xticks(rotation=45, ha='right')
+    plt.axhline(y=0, color='gray', linestyle='--')
+    plt.title(f"Character Sentiment Comparison in {season}")
+    plt.xlabel("Episode")
+    plt.ylabel("Sentiment Polarity")
+    plt.legend()
+    plt.grid()
+    plt.show()
+
+compare_character_sentiment("Demon Slayer S.4", ["Tanjirou", "Zenitsu", "Inosuke"])
+
+
+# In[ ]:
+
+
+
+