Sentiment Analysis Overview

What is Sentiment Analysis?

• Sentiment analysis is the process of analyzing large volumes of text to determine sentiment, whether positive, negative, or neutral.
• Helps companies understand customer opinions, improve experiences, and enhance brand reputation.

Basis of Sentiment Analysis

• Built on Natural Language Processing (NLP) to mimic human text interpretation.

Approaches to Sentiment Analysis

1. Rule-Based Approach

• Classifies text based on keywords grouped into lexicons (e.g., 'affordable', 'well-made' as positive).
• Calculates sentiment score based on frequency and context of these words.
• Challenges:
  • Sarcasm: Difficult to detect, may lead to misclassification (e.g., sarcastic positive words).
  • Negation: Words like 'not inexpensive' can convey opposite meaning, creating interpretation issues.
  • Idiomatic Language: Phrases like 'break a leg' can be misinterpreted.

2. Machine Learning-Based Approach

• Uses algorithms to recognize language patterns and complexities.
• Common Algorithms:
  • Linear Regression: Predicts sentiment score via text features like word frequency, review length.
  • Naive Bayes: Uses Bayes theorem to calculate sentiment probability based on word occurrences.
  • Support Vector Machines (SVM): Solves two-group classification problems, identifies boundary between positive and negative reviews.

Types of Sentiment Analysis

1. Polarity Analysis

• Measures overall sentiment on a scale (positive to negative).

2. Fine-Grained Sentiment Analysis

• Provides numerical rating on a scale from 0 (neutral) to 100 (extreme sentiment).

3. Aspect-Based Sentiment Analysis (ABSA)

• Focuses on specific aspects of a product/service (e.g., user feedback on a particular feature).

4. Emotion Detection

• Seeks to understand the psychological state or specific emotions in text (e.g., frustration).
• Helps identify emotions beyond simple polarity.

Benefits and Applications

• Helps businesses make meaningful changes by understanding customer sentiment and emotion.
• Useful for prioritizing support issues, conducting market research, and identifying new opportunities.
• When executed correctly, sentiment analysis can translate complex reviews into actionable insights.

Conclusion

• Sentiment analysis is a powerful tool for guiding business decisions and understanding customer feedback.
• For further inquiries or interest in similar content, viewers are encouraged to engage.