Sentiment Analysis

Overview

感情分析はテキストの感情トーンと意見を決定し、顧客満足度、ブランド認識、フィードバック分析の理解を可能にします。

アプローチ

• 辞書ベース: 感情辞書を使用
• 機械学習: ラベル付きデータでの分類器の訓練
• 深層学習: 複雑なパターン認識のためのニューラルネットワーク
• アスペクトベース: 特定の機能に関する感情
• 多言語対応: 英語以外のテキスト分析

感情タイプ

• ポジティブ: 好意的、満足
• ネガティブ: 否定的、不満
• ニュートラル: 事実的、明確な感情なし
• 混合: 複数の感情の組み合わせ

Python での実装

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import re
from collections import Counter

# Sample review data
reviews_data = [
    "This product is amazing! I love it so much.",
    "Terrible quality, very disappointed.",
    "It's okay, nothing special.",
    "Best purchase ever! Highly recommend.",
    "Worst product I've ever bought.",
    "Pretty good, satisfied with the purchase.",
    "Excellent service and fast delivery.",
    "Poor quality and bad customer support.",
    "Not bad, does what it's supposed to.",
    "Absolutely fantastic! Five stars!",
    "Mediocre product, expected better.",
    "Love everything about this!",
    "Complete waste of money.",
    "Good value for the price.",
    "Very satisfied, will buy again!",
    "Horrible experience from start to finish.",
    "It works as described.",
    "Outstanding quality and design!",
    "Disappointed with the results.",
    "Perfect! Exactly what I wanted.",
]

sentiments = [
    'Positive', 'Negative', 'Neutral', 'Positive', 'Negative',
    'Positive', 'Positive', 'Negative', 'Neutral', 'Positive',
    'Negative', 'Positive', 'Negative', 'Positive', 'Positive',
    'Negative', 'Neutral', 'Positive', 'Negative', 'Positive'
]

df = pd.DataFrame({'review': reviews_data, 'sentiment': sentiments})

print("Sample Reviews:")
print(df.head(10))

# 1. Lexicon-based Sentiment Analysis
from nltk.sentiment import SentimentIntensityAnalyzer
try:
    import nltk
    nltk.download('vader_lexicon', quiet=True)
    sia = SentimentIntensityAnalyzer()

    df['vader_scores'] = df['review'].apply(lambda x: sia.polarity_scores(x))
    df['vader_compound'] = df['vader_scores'].apply(lambda x: x['compound'])
    df['vader_sentiment'] = df['vader_compound'].apply(
        lambda x: 'Positive' if x > 0.05 else ('Negative' if x < -0.05 else 'Neutral')
    )

    print("\n1. VADER Sentiment Scores:")
    print(df[['review', 'vader_compound', 'vader_sentiment']].head())
except:
    print("NLTK not available, skipping VADER analysis")

# 2. Textblob Sentiment (alternative)
try:
    from textblob import TextBlob

    df['textblob_polarity'] = df['review'].apply(lambda x: TextBlob(x).sentiment.polarity)
    df['textblob_sentiment'] = df['textblob_polarity'].apply(
        lambda x: 'Positive' if x > 0.1 else ('Negative' if x < -0.1 else 'Neutral')
    )

    print("\n2. TextBlob Sentiment Scores:")
    print(df[['review', 'textblob_polarity', 'textblob_sentiment']].head())
except:
    print("TextBlob not available")

# 3. Feature Extraction for ML
vectorizer = TfidfVectorizer(max_features=100, stop_words='english')
X = vectorizer.fit_transform(df['review'])
y = df['sentiment']

print(f"\n3. Feature Matrix Shape: {X.shape}")
print(f"Features extracted: {len(vectorizer.get_feature_names_out())}")

# 4. Machine Learning Model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Naive Bayes classifier
nb_model = MultinomialNB()
nb_model.fit(X_train, y_train)
y_pred = nb_model.predict(X_test)

print("\n4. Machine Learning Results:")
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")
print("\nClassification Report:")
print(classification_report(y_test, y_pred))

# 5. Sentiment Distribution
fig, axes = plt.subplots(2, 2, figsize=(14, 8))

# Distribution of sentiments
sentiment_counts = df['sentiment'].value_counts()
axes[0, 0].bar(sentiment_counts.index, sentiment_counts.values, color=['green', 'red', 'gray'], alpha=0.7, edgecolor='black')
axes[0, 0].set_title('Sentiment Distribution')
axes[0, 0].set_ylabel('Count')
axes[0, 0].grid(True, alpha=0.3, axis='y')

# Pie chart
axes[0, 1].pie(sentiment_counts.values, labels=sentiment_counts.index, autopct='%1.1f%%',
               colors=['green', 'red', 'gray'], startangle=90)
axes[0, 1].set_title('Sentiment Proportion')

# VADER compound scores distribution
if 'vader_compound' in df.columns:
    axes[1, 0].hist(df['vader_compound'], bins=20, color='steelblue', edgecolor='black', alpha=0.7)
    axes[1, 0].axvline(x=0.05, color='green', linestyle='--', label='Positive threshold')
    axes[1, 0].axvline(x=-0.05, color='red', linestyle='--', label='Negative threshold')
    axes[1, 0].set_xlabel('VADER Compound Score')
    axes[1, 0].set_ylabel('Frequency')
    axes[1, 0].set_title('VADER Score Distribution')
    axes[1, 0].legend()

# Confusion matrix
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[1, 1],
            xticklabels=np.unique(y_test), yticklabels=np.unique(y_test))
axes[1, 1].set_title('Classification Confusion Matrix')
axes[1, 1].set_ylabel('True Label')
axes[1, 1].set_xlabel('Predicted Label')

plt.tight_layout()
plt.show()

# 6. Most Informative Features
feature_names = vectorizer.get_feature_names_out()

# Get feature importance from Naive Bayes
for sentiment_class, idx in enumerate(np.unique(y)):
    class_idx = list(np.unique(y)).index(idx)
    top_features_idx = np.argsort(nb_model.feature_log_prob_[class_idx])[-5:]

    print(f"\nTop features for '{idx}':")
    for feature_idx in reversed(top_features_idx):
        print(f"  {feature_names[feature_idx]}")

# 7. Word frequency analysis
positive_words = ' '.join(df[df['sentiment'] == 'Positive']['review'].values).lower()
negative_words = ' '.join(df[df['sentiment'] == 'Negative']['review'].values).lower()

# Clean and tokenize
def get_words(text):
    text = re.sub(r'[^a-z\s]', '', text)
    words = text.split()
    return [w for w in words if len(w) > 2]

pos_word_freq = Counter(get_words(positive_words))
neg_word_freq = Counter(get_words(negative_words))

# Visualization
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Positive words
top_pos_words = dict(pos_word_freq.most_common(10))
axes[0].barh(list(top_pos_words.keys()), list(top_pos_words.values()), color='green', alpha=0.7, edgecolor='black')
axes[0].set_xlabel('Frequency')
axes[0].set_title('Top Words in Positive Reviews')
axes[0].invert_yaxis()

# Negative words
top_neg_words = dict(neg_word_freq.most_common(10))
axes[1].barh(list(top_neg_words.keys()), list(top_neg_words.values()), color='red', alpha=0.7, edgecolor='black')
axes[1].set_xlabel('Frequency')
axes[1].set_title('Top Words in Negative Reviews')
axes[1].invert_yaxis()

plt.tight_layout()
plt.show()

# 8. Trend analysis (simulated over time)
dates = pd.date_range('2023-01-01', periods=len(df))
df['date'] = dates
df['rolling_sentiment_score'] = df['vader_compound'].rolling(window=3, center=True).mean()

fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(df['date'], df['rolling_sentiment_score'], marker='o', linewidth=2, label='Rolling Avg (3-day)')
ax.scatter(df['date'], df['vader_compound'], alpha=0.5, s=30, label='Daily Score')
ax.axhline(y=0, color='black', linestyle='--', alpha=0.3)
ax.fill_between(df['date'], df['rolling_sentiment_score'], 0,
                where=(df['rolling_sentiment_score'] > 0), alpha=0.3, color='green', label='Positive')
ax.fill_between(df['date'], df['rolling_sentiment_score'], 0,
                where=(df['rolling_sentiment_score'] <= 0), alpha=0.3, color='red', label='Negative')
ax.set_xlabel('Date')
ax.set_ylabel('Sentiment Score')
ax.set_title('Sentiment Trend Over Time')
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

# 9. Aspect-based sentiment (simulated)
aspects = ['Quality', 'Price', 'Delivery', 'Customer Service']
aspect_sentiments = []

for aspect in aspects:
    keywords = {
        'Quality': ['quality', 'product', 'design', 'material'],
        'Price': ['price', 'cost', 'expensive', 'value'],
        'Delivery': ['delivery', 'fast', 'shipping', 'arrived'],
        'Customer Service': ['service', 'support', 'customer', 'help']
    }

    # Count mentions and associated sentiment
    aspect_reviews = df[df['review'].str.contains('|'.join(keywords[aspect]), case=False)]
    if len(aspect_reviews) > 0:
        avg_sentiment = aspect_reviews['vader_compound'].mean()
        aspect_sentiments.append({
            'Aspect': aspect,
            'Avg Sentiment': avg_sentiment,
            'Count': len(aspect_reviews)
        })

aspect_df = pd.DataFrame(aspect_sentiments)

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Aspect sentiment scores
colors_aspect = ['green' if x > 0 else 'red' for x in aspect_df['Avg Sentiment']]
axes[0].barh(aspect_df['Aspect'], aspect_df['Avg Sentiment'], color=colors_aspect, alpha=0.7, edgecolor='black')
axes[0].set_xlabel('Average Sentiment Score')
axes[0].set_title('Sentiment by Aspect')
axes[0].axvline(x=0, color='black', linestyle='-', linewidth=0.8)
axes[0].grid(True, alpha=0.3, axis='x')

# Mention count
axes[1].bar(aspect_df['Aspect'], aspect_df['Count'], color='steelblue', alpha=0.7, edgecolor='black')
axes[1].set_ylabel('Number of Mentions')
axes[1].set_title('Aspect Mention Frequency')
axes[1].grid(True, alpha=0.3, axis='y')

plt.tight_layout()
plt.show()

# 10. Summary report
print("\n" + "="*50)
print("SENTIMENT ANALYSIS SUMMARY")
print("="*50)
print(f"Total Reviews: {len(df)}")
print(f"Positive: {len(df[df['sentiment'] == 'Positive'])} ({len(df[df['sentiment'] == 'Positive'])/len(df)*100:.1f}%)")
print(f"Negative: {len(df[df['sentiment'] == 'Negative'])} ({len(df[df['sentiment'] == 'Negative'])/len(df)*100:.1f}%)")
print(f"Neutral: {len(df[df['sentiment'] == 'Neutral'])} ({len(df[df['sentiment'] == 'Neutral'])/len(df)*100:.1f}%)")
if 'vader_compound' in df.columns:
    print(f"\nAverage VADER Score: {df['vader_compound'].mean():.3f}")
    print(f"Model Accuracy: {accuracy_score(y_test, y_pred):.2%}")
print("="*50)

手法の比較

• 辞書ベース: 高速、解釈可能、文脈に限界あり
• 機械学習: 訓練データが必要、精度が良い
• 深層学習: 複雑なパターン認識、大規模データセット必要
• ハイブリッド: 複数のアプローチを組み合わせ

応用例

• 顧客フィードバック分析
• 製品レビュー監視
• ソーシャルメディア感情分析
• ブランド認識追跡
• チャットボット感情検出

成果物

• 感情分布の分析
• すべてのテキストの感情分類
• 信頼度スコア
• 分類の特徴重要度
• トレンド分析の可視化
• アスペクトベースの感情分析
• インサイト付きエグゼクティブサマリー