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Sentiment Analysis Overview

Sentiment analysis, also known as opinion mining or emotion AI, involves analyzing the affective states and subjective information expressed in natural language. It is used across various domains such as voice of customers, healthcare, and social media to identify positive, negative, or neutral sentiments.

Simple Cases

  1. "Coronet has the best lines of all day cruisers."

    • Sentiment: Positive
    • Reason: Expresses satisfaction with the product.

  2. "Bertram has a deep V hull and runs easily through seas."

    • Sentiment: Neutral
    • Reason: Describes a product's features without strong opinion.

  3. "Pastel-colored 1980s day cruisers from Florida are ugly."

    • Sentiment: Negative
    • Reason: Expresses dissatisfaction.

  4. "I dislike old cabin cruisers."

    • Sentiment: Negative
    • Reason: Direct expression of dislike.

More Challenging Examples

  1. "I do not dislike cabin cruisers." (Negation handling)

    • Sentiment: Neutral
    • Reason: Demonstrates negation.

  2. "Disliking watercraft is not really my thing." (Negation, inverted word order)

    • Sentiment: Neutral
    • Reason: Inverted negation indicates a change in attitude.

  3. "Sometimes I really hate RIBs." (Adverbial modifies sentiment)

    • Sentiment: Negative
    • Reason: Adverb modifies the sentiment tone.

  4. "I'd really truly love going out in this weather!"

    • Sentiment: Positive
    • Reason: Slightly sarcastic, but positive sentiment.

  5. "Chris Craft is better looking than Limestone." (Two brand names, identifying the target)

    • Sentiment: Neutral
    • Reason: Two brands, but the target is not clearly identified.

  6. "Chris Craft is better looking than Limestone, but Limestone projects seaworthiness and reliability."

    • Sentiment: Mixed
    • Reason: Two attitudes, with a focus on both brands.

Types of Sentiment Analysis

  • Basic Task: Classifying sentiment at document, sentence, or feature/aspect levels (positive, negative, neutral).
  • Advanced Task: Emotional states such as enjoyment, anger, etc.

Precursors to Sentiment Analysis

  • General Inquirer: Provided hints about quantifying patterns.
  • Psychological research: Examined verbal behavior for psychological state.

Subsequent Methods

  • EffectCheck: Uses synonym scales.
  • Turney and Pang: Detecting polarity on document level.

Advanced Techniques

  • Pang and Lee: Predicting star ratings on 3/4 star scale.
  • Snyder: Predicting restaurant ratings.

Challenges in Sentiment Analysis

  • Neutral class: Ignored in binary models.
  • Three-way classification: Uses a neutral class for better accuracy.
  • Neutral vs. Positive: Manual filtering for clarity.

Sentiment Systems

  • Max Entropy and SVM: Improve accuracy with a neutral class.
  • Bootstrapping Methods: Automatically identify patterns.

Applications

  • Business: Customer feedback analysis.
  • Finance: Stock price prediction.
  • Social science: Students' feedback.

Feature-Based Sentiment Analysis

  • Features: Analyzing sentiment on different aspects (e.g., product features).

Intensity Ranking

  • Sentiment Intensity: Subjective, varying per document.

Methods and Features

  • Knowledge-Based: Uses affect words.
  • Statistical: Bag-of-words models.
  • Hybrid: Combines machine learning with ontologies.

Ethical Considerations

  • Privacy: Analyzes personal data without consent.
  • Bias: Develops ethical frameworks (e.g., SEWA).