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What Is Sentiment Analysis?

Sentiment Analysis is a Natural Language Processing technique that identifies and categorizes opinions in text as positive, negative, or neutral. It is commonly used for social media monitoring, customer feedback analysis, brand reputation management, product analysis, and stock market prediction. Techniques include rule-based systems, machine learning models, and deep learning models, with output types being binary, multiclass, or score-based.

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28 views2 pages

What Is Sentiment Analysis?

Sentiment Analysis is a Natural Language Processing technique that identifies and categorizes opinions in text as positive, negative, or neutral. It is commonly used for social media monitoring, customer feedback analysis, brand reputation management, product analysis, and stock market prediction. Techniques include rule-based systems, machine learning models, and deep learning models, with output types being binary, multiclass, or score-based.

Uploaded by

muzamil aslam roll no 9
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We take content rights seriously. If you suspect this is your content, claim it here.
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💬 What is Sentiment Analysis?

Sentiment Analysis is a Natural Language Processing (NLP) technique used to determine the
emotional tone or opinion expressed in a piece of text.

✅ Definition:

Sentiment Analysis is the process of identifying and categorizing opinions expressed in text —
especially to determine whether the writer's attitude is positive, negative, or neutral.

🧠 Example:

 Input: “I love the features of this new phone!”


 Output: Positive Sentiment
 Input: “The customer service was terrible and unhelpful.”
 Output: Negative Sentiment

🔍 Common Use Cases:

1. Social Media Monitoring – Analyzing tweets or Facebook comments to gauge public


opinion.
2. Customer Feedback Analysis – Understanding reviews on Amazon, Flipkart, Zomato,
etc.
3. Brand Reputation Management – Checking overall sentiment about a brand online.
4. Product Analysis – Categorizing reviews to improve features.
5. Stock Market Prediction – Using news sentiment to predict investor behavior.

🛠️Techniques Used:

 Rule-based Systems – Using predefined lists of positive/negative words.


 Machine Learning Models – Trained classifiers (e.g., Logistic Regression, SVM, Naive
Bayes).
 Deep Learning Models – LSTMs, BERT, and other transformers for contextual
understanding.

🧪 Output Types:
 Binary: Positive or Negative
 Multiclass: Positive, Negative, Neutral
 Score-based: Sentiment score from -1 (very negative) to +1 (very positive)

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