AI Lesson X – Lesson Title | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 75: Sentiment Analysis

Sentiment Analysis is one of the most widely used applications of Natural Language Processing. It focuses on understanding the emotion or opinion expressed in text.

In simple terms, sentiment analysis answers one question: is the text positive, negative, or neutral?

Real-World Connection

Companies analyze customer reviews to understand satisfaction, brands track social media to monitor public opinion, and governments analyze feedback to understand citizen sentiment.

Whenever a product review is marked as positive or negative automatically, sentiment analysis is being used.

What Is Sentiment Analysis?

Sentiment analysis is the process of identifying emotional tone in text. The model learns patterns that indicate happiness, anger, disappointment, or neutrality.

  • Positive sentiment — approval or satisfaction
  • Negative sentiment — complaint or dissatisfaction
  • Neutral sentiment — factual or emotionless text

How Sentiment Analysis Works

Modern sentiment analysis systems use pretrained language models like BERT, RoBERTa, or DistilBERT. These models understand context and word relationships instead of relying on simple keyword matching.

The model reads the entire sentence and predicts sentiment based on learned patterns.

Simple Sentiment Analysis Example

Below is an example of using a pretrained NLP model to analyze sentiment. 


from transformers import pipeline

sentiment_model = pipeline("sentiment-analysis")

text = "Dataplexa explains AI concepts very clearly"
result = sentiment_model(text)

print(result)
[{'label': 'POSITIVE', 'score': 0.99}]

Understanding the Code

The pipeline loads a pretrained sentiment analysis model. The input sentence is tokenized and passed through transformer layers.

The output contains the predicted sentiment label along with a confidence score that indicates how certain the model is.

Handling Negative Sentiment

Let’s analyze a negative example. 


text = "The service was slow and disappointing"
result = sentiment_model(text)

print(result)
[{'label': 'NEGATIVE', 'score': 0.97}]

Common Use Cases

  • Customer review analysis
  • Social media monitoring
  • Product feedback analysis
  • Survey response analysis

Challenges in Sentiment Analysis

  • Sarcasm and irony
  • Mixed emotions in one sentence
  • Domain-specific language

Advanced models reduce these issues by learning deeper context.

Practice Questions

Practice 1: What does sentiment analysis identify in text?



Practice 2: What sentiment does the sentence “I love this product” express?



Practice 3: Which models are commonly used for sentiment analysis today?



Quick Quiz

Quiz 1: Which NLP task detects opinions in text?





Quiz 2: What does the score in sentiment output represent?





Quiz 3: Which challenge is hardest for sentiment analysis?





Coming up next: Named Entity Recognition — identifying people, places, and organizations in text.

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