Course/Module 6/Topic 3 of 5Beginner

Sentiment Analysis

Learn to classify text sentiment using deep learning. This is a foundational concept in artificial intelligence and machine learning that professional developers rely on daily. The explanations below are written to be beginner-friendly while covering the depth and nuance that comes from real-world AI/ML experience. Take your time with each section and practice the examples

60 min•By Priygop Team•Last updated: Feb 2026

What is Sentiment Analysis?

A task to determine whether text expresses positive, negative, or neutral emotions.. This is an essential concept that every AI/ML developer must understand thoroughly. In professional development environments, getting this right can mean the difference between code that works reliably and code that breaks in production. The following sections break this down into clear, digestible pieces with practical examples you can try immediately

Implementation with Keras

Example

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np

sentences = ["I love NLP", "I hate spam emails"]
labels = np.array([1, 0])  # 1: Positive, 0: Negative

tokenizer = Tokenizer(num_words=1000)
tokenizer.fit_on_texts(sentences)
sequences = tokenizer.texts_to_sequences(sentences)
padded = pad_sequences(sequences, maxlen=5)

model = keras.Sequential([
    keras.layers.Embedding(1000, 16, input_length=5),
    keras.layers.GlobalAveragePooling1D(),
    keras.layers.Dense(16, activation='relu'),
    keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(padded, labels, epochs=10, verbose=1)

Try It Yourself — Sentiment Analysis

Try It Yourself — Sentiment AnalysisJavaScript

JavaScript Editor

✓ ValidTab = 2 spaces

// AI & Machine Learning concepts in JavaScript
// Simple linear regression simulation

// Training data: [hours studied] -> [score]
const data = [
  [1, 50], [2, 55], [3, 65], [4, 70],
  [5, 75], [6, 80], [7, 85], [8, 90],
];

// Calculate linear regression (y = mx + b)
function linearRegression(data) {
  const n = data.length;
  const sumX = data.reduce((s, [x]) => s + x, 0);
  const sumY = data.reduce((s, [, y]) => s + y, 0);
  const sumXY = data.reduce((s, [x, y]) => s + x * y, 0);
  const sumX2 = data.reduce((s, [x]) => s + x * x, 0);
  const m = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);
  const b = (sumY - m * sumX) / n;
  return { m, b };
}

const { m, b } = linearRegression(data);
console.log(`Model: score = ${m.toFixed(2)} * hours + ${b.toFixed(2)}`);

// Predict
function predict(hours) {
  return Math.round(m * hours + b);
}

console.log("Predictions:");
[3, 5, 7, 10].forEach(h => {
  console.log(`  ${h} hours → predicted score: ${predict(h)}`);
});

Result

Click ▶ Run to see the result

Edit the code on the left, then click Run

JavaScript|33 lines|986 chars|✓ Valid syntax

UTF-8

Topics in This Module

Course/Module 6/Topic 3 of 5Beginner

Sentiment Analysis

60 min•By Priygop Team•Last updated: Feb 2026

What is Sentiment Analysis?

Implementation with Keras

Example

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np

sentences = ["I love NLP", "I hate spam emails"]
labels = np.array([1, 0])  # 1: Positive, 0: Negative

tokenizer = Tokenizer(num_words=1000)
tokenizer.fit_on_texts(sentences)
sequences = tokenizer.texts_to_sequences(sentences)
padded = pad_sequences(sequences, maxlen=5)

model = keras.Sequential([
    keras.layers.Embedding(1000, 16, input_length=5),
    keras.layers.GlobalAveragePooling1D(),
    keras.layers.Dense(16, activation='relu'),
    keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(padded, labels, epochs=10, verbose=1)

Try It Yourself — Sentiment Analysis

Try It Yourself — Sentiment AnalysisJavaScript

JavaScript Editor

✓ ValidTab = 2 spaces

// AI & Machine Learning concepts in JavaScript
// Simple linear regression simulation

// Training data: [hours studied] -> [score]
const data = [
  [1, 50], [2, 55], [3, 65], [4, 70],
  [5, 75], [6, 80], [7, 85], [8, 90],
];

// Calculate linear regression (y = mx + b)
function linearRegression(data) {
  const n = data.length;
  const sumX = data.reduce((s, [x]) => s + x, 0);
  const sumY = data.reduce((s, [, y]) => s + y, 0);
  const sumXY = data.reduce((s, [x, y]) => s + x * y, 0);
  const sumX2 = data.reduce((s, [x]) => s + x * x, 0);
  const m = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);
  const b = (sumY - m * sumX) / n;
  return { m, b };
}

const { m, b } = linearRegression(data);
console.log(`Model: score = ${m.toFixed(2)} * hours + ${b.toFixed(2)}`);

// Predict
function predict(hours) {
  return Math.round(m * hours + b);
}

console.log("Predictions:");
[3, 5, 7, 10].forEach(h => {
  console.log(`  ${h} hours → predicted score: ${predict(h)}`);
});

Result

Click ▶ Run to see the result

Edit the code on the left, then click Run

JavaScript|33 lines|986 chars|✓ Valid syntax

UTF-8

Topics in This Module

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