10 _Sentiment-Analysis-using-Machine-Learning.pptx

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1. Sentiment Analysis using Machine Learning Presented by: Mohit R Chawla/D12C/10 Course: Machine Learning

2. What is Sentiment Analysis? Sentiment Analysis, also known as Opinion Mining , is the process of computationally identifying and categorizing opinions expressed in a piece of text, determining the writer’s attitude toward a particular topic or product. Core Concept Uses Natural Language Processing (NLP) and Machine Learning (ML) to extract subjective information. Classification Goal Typically classifies text into polarities: Positive , Negative , or Neutral. Why It Matters Now The sheer volume of digital data—tweets, reviews, comments—requires automated analysis for effective insight generation.

3. Sentiment Analysis in Action: Real-World Use Cases ML-driven sentiment analysis provides crucial insights across massive, diverse datasets. Social Media Monitoring Tracking public sentiment about brands, events, or political figures in real-time across platforms like X (Twitter). E-commerce Reviews Automatically summarizing millions of product reviews on sites like Amazon to identify key satisfaction drivers and pain points. Media Engagement Analyzing user comments on YouTube or news articles to gauge audience reception to content or campaigns.

4. How Machine Learning Classifies Sentiment ML models learn to map text patterns to emotional labels through supervised training. Basic Classification The model processes input text and assigns a probability score to each class (Positive, Negative, Neutral). Simple models use statistical relationships between words and sentiment scores from labeled training data. Text becomes numerical features the algorithm can understand. The accuracy relies heavily on the quality and size of the initial human-labeled dataset.

5. Key ML Techniques for Sentiment Different models offer varying levels of complexity and ability to capture contextual nuance. 1 Naive Bayes (Statistical) A probabilistic classifier based on Bayes’ Theorem. It assumes feature independence (a word's presence doesn't affect another's), making it fast and effective for simple text classification. 2 Logistic Regression (Linear) A linear model used for binary or multi-class classification. It predicts the probability of an input belonging to a certain class. 3 LSTMs (Deep Learning) Long Short-Term Memory networks (a type of Recurrent Neural Network) are used to understand context and sequence in text, crucial for handling longer reviews and complex sentences.

6. The Workflow: From Raw Data to Insight Sentiment Analysis follows a predictable pipeline to convert unstructured text into actionable data. Output Model Training Feature Extraction Preprocessing Raw Data Crucial Preprocessing Steps: Tokenization: Breaking text into smaller units (words or phrases). Stop Word Removal: Eliminating common words (e.g., "the," "a," "is") that don't add semantic value.

7. Advantages & Limitations of ML Sentiment Advantages: Why ML Excels Speed and Scale: Processes millions of documents faster than human analysts. Consistency: Applies rules uniformly, eliminating human subjective bias. High Accuracy: Achieves high precision on straightforward, domain-specific text. Limitations: The Challenges Sarcasm & Irony: Difficulty in interpreting when words are used to mean their opposite. Context & Negation: Struggles with subtle negations (e.g., "not bad") and domain-specific context (e.g., "slow" is positive for a phone battery but negative for a network). Data Dependency: Requires large amounts of expensive, human-labeled data for training.

8. The Future Scope: Next Frontiers Current research is pushing beyond simple polarity to deeper, more nuanced understanding of human communication. Emotion Detection Moving from classifying 'Positive/Negative' to specific emotions like joy, anger, sadness , or surprise . Multilingual Analysis Developing models robust enough to accurately analyze sentiment across multiple languages and dialects without extensive retraining. Chatbot Integration Using real-time sentiment analysis to allow AI assistants to detect user frustration and adjust their conversational style or escalation path instantly.

9. Conclusion: Understanding the Digital Voice ML Bridges the Gap Between Text and Meaning Key Takeaway Sentiment analysis transforms raw text data into quantifiable insights about human opinions and behaviors. Empowerment It provides businesses, governments, and researchers the power to understand and respond to the collective digital voice. Continuing Challenge The pursuit of context and nuance (like sarcasm) ensures ML remains a dynamic, evolving field of study.