extract

**Sentiment Analysis** **Overview** Sentiment Analysis (Opinion Mining) is the use of NLP to systematically identify, extract, and quantify affective states and subjective information. It determines if a piece of writing is positive, negative, or neutral. **Levels of Analysis** **1. Document Level** Classifying the whole document. - "This review is Positive." **2. Sentence Level** Classifying each sentence. - "The screen is great." (Positive) - "But the battery sucks." (Negative) **3. Aspect-Based Sentiment Analysis (ABSA)** linking sentiment to specific attributes (Aspects). - Entity: iPhone - Aspect: Battery → Sentiment: Negative - Aspect: Screen → Sentiment: Positive **Approaches** **Rule-Based (VADER)** Uses a dictionary of word scores ("Good" = +1.9, "Bad" = -1.5) and rules for amplifiers ("Very good" > "Good") and negations ("Not good" is negative). - Good for social media (handles emojis/slang). - Fast/Cheap. **Machine Learning (BERT/RoBERTa)** Deep learning models trained to "read" the full context. - "The movie was not unpredictable." → Positive (Double negative). - SOTA accuracy. **Use Cases** - **Brand Monitoring**: Tracking Twitter/X mentions during a PR crisis. - **Stock Trading**: Analyzing news headlines to predict market movement. - **Customer Support**: Prioritizing angry tickets. - **Product Analysis**: Aggregating pros/cons from Amazon reviews. **Challenges** - **Sarcasm**: "Great, my phone died again." (Machine sees "Great", human sees sarcasm). - **Nuance**: "The movie was shorter than I expected." (Good or Bad?). Sentiment analysis turns unstructured voice-of-customer data into tracked metrics.