Sentiment Analysis

Sentiment analysis, also known as opinion mining, is a subfield of Natural Language Processing (NLP) that uses computational techniques to systematically identify, extract, and quantify the emotional tone or subjective information within a piece of text. As a critical tool in data science, it applies machine learning algorithms and statistical models to classify the sentiment of data from sources like social media, customer reviews, and surveys as positive, negative, or neutral. This process is essential for transforming vast amounts of unstructured text into structured, actionable insights, enabling organizations to gauge public opinion, monitor brand reputation, and understand customer feedback at scale.

1. Introduction to Sentiment Analysis
   1. Definition and Scope
      1. Sentiment Analysis Definition
      2. Opinion Mining Definition
      3. Relationship Between Sentiment Analysis and Opinion Mining
      4. Historical Development
   2. Core Concepts and Terminology
      1. Subjectivity vs Objectivity
      2. Polarity Classification
         1. Positive Sentiment
         2. Negative Sentiment
         3. Neutral Sentiment
         4. Mixed Sentiment
      3. Emotion vs Sentiment
      4. Tone Analysis
      5. Sentiment Intensity
      6. Opinion Holder
      7. Opinion Target
      8. Opinion Expression
   3. Applications and Use Cases
      1. Business Intelligence
         1. Competitive Analysis
         2. Product Development
         3. Market Positioning
      2. Brand Monitoring
         1. Reputation Management
         2. Crisis Detection
         3. Brand Perception Analysis
      3. Customer Experience Management
         1. Product Reviews Analysis
         2. Service Feedback Analysis
         3. Customer Satisfaction Measurement
      4. Market Research
         1. Consumer Behavior Analysis
         2. Trend Analysis
         3. Consumer Insights
      5. Social Media Analytics
         1. Social Listening
         2. Influencer Analysis
         3. Viral Content Analysis
      6. Political and Social Analysis
         1. Public Opinion Tracking
         2. Election Prediction
         3. Policy Impact Assessment
         4. Social Movement Analysis
      7. Healthcare Applications
         1. Patient Feedback Analysis
         2. Mental Health Monitoring
         3. Drug Review Analysis
      8. Financial Applications
         1. Stock Market Prediction
         2. Financial News Analysis
         3. Investment Sentiment
   4. Levels of Sentiment Analysis
      1. Document-Level Analysis
      2. Sentence-Level Analysis
      3. Aspect-Level Analysis
      4. Sub-sentence Level Analysis
      5. Entity-Level Analysis
      6. Comparative Sentiment Analysis