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What is Sentiment Analysis? A Complete Guide for Beginners

· 12 min read
Kobkrit Viriyayudhakorn
Kobkrit Viriyayudhakorn
CEO @ iApp Technology

When a customer writes "สินค้าดีมาก ส่งเร็ว ประทับใจ!" (Great product, fast delivery, impressed!) in a review, how does your business automatically know this is positive feedback? When thousands of social media comments flood in after a product launch, how can you quickly understand whether people love it or hate it? The answer is Sentiment Analysis — one of the most practical applications of AI in business today.

What is Sentiment Analysis?​

Sentiment Analysis (also called Opinion Mining) is an AI technique that automatically identifies and extracts emotions, opinions, and attitudes from text data. It uses Natural Language Processing (NLP) and machine learning to determine whether a piece of text expresses positive, negative, or neutral sentiment.

Think of sentiment analysis as having thousands of assistants who can instantly read and understand the emotional tone behind every message, review, or comment your business receives.

At its core, sentiment analysis:

  • Analyzes text data to determine emotional tone
  • Classifies opinions as positive, negative, or neutral
  • Quantifies subjective information into measurable data
  • Scales to process millions of texts automatically

Simple Analogy​

Imagine you own a restaurant and want to know what customers think:

  • Manual approach: Read every review yourself, judge the sentiment, take notes
  • Sentiment analysis: AI reads all reviews instantly and tells you "85% positive, 10% neutral, 5% negative"

The AI doesn't just count keywords — it understands context, sarcasm, and even mixed sentiments like "The food was great but the service was slow."

How Sentiment Analysis Works​

How Sentiment Analysis Works - Workflow Diagram

The Process Step-by-Step​

1. Data Collection

  • Gather text from reviews, social media, surveys, support tickets
  • Can handle single texts or millions of documents

2. Text Preprocessing

  • Tokenization: Breaking text into individual words or phrases
  • Cleaning: Removing noise, special characters, stopwords
  • Normalization: Converting text to standard format

3. Feature Extraction

  • Converting text into numerical representations
  • Modern systems use word embeddings or transformer models
  • Captures semantic meaning, not just keywords

4. Sentiment Classification

  • AI model analyzes the processed text
  • Determines sentiment polarity and confidence score
  • May detect specific aspects or emotions

5. Output Results

  • Returns sentiment label (positive/negative/neutral)
  • Provides confidence score (e.g., 85% positive)
  • Can include detailed breakdowns by aspect or emotion

Types of Sentiment Analysis​

Types of Sentiment Analysis

1. Standard Sentiment Analysis (Polarity Detection)​

What it does: Classifies text into three categories: positive, negative, or neutral.

Example:

  • "ร้านนี้อาหารอร่อยมาก" → Positive
  • "บริการแย่มาก รอนาน" → Negative
  • "ร้านเปิด 9 โมงเช้า" → Neutral

Best for: Quick overview of customer sentiment, review ratings, feedback classification

2. Aspect-Based Sentiment Analysis (ABSA)​

What it does: Identifies sentiment toward specific aspects or features within a text.

Example: Review: "อาหารอร่อย แต่บริการช้ามาก ราคาเหมาะสม"

  • Food quality: Positive
  • Service speed: Negative
  • Price: Positive

Best for: Product feedback, service evaluation, detailed customer insights

3. Emotion Detection​

What it does: Goes beyond positive/negative to identify specific emotions like joy, anger, fear, sadness, or surprise.

Example:

  • "ตื่นเต้นมากกับสินค้าใหม่!" → Joy/Excitement
  • "ผิดหวังมากที่ของไม่มาตามนัด" → Disappointment/Anger

Best for: Customer experience analysis, brand perception, crisis management

4. Multilingual Sentiment Analysis​

What it does: Analyzes sentiment across multiple languages with language-specific understanding.

Example: Thai text: "สินค้าดีมาก" → Positive (Thai model understands Thai idioms and expressions)

Best for: Global brands, international customer feedback, cross-border e-commerce

Key Terms Explained (Jargon Buster)​

1. Polarity​

What it is: The direction of sentiment — whether it's positive, negative, or neutral.

Simple explanation: Like the poles of a magnet, polarity tells you which "direction" the opinion leans. Positive polarity means favorable opinion, negative means unfavorable.

Example:

  • "รักเลย!" → Positive polarity (+1)
  • "เกลียด!" → Negative polarity (-1)
  • "ปกติ" → Neutral polarity (0)

2. Confidence Score​

What it is: A percentage (0-100%) indicating how certain the AI model is about its sentiment classification.

Simple explanation: Think of it as the AI's "confidence level." A 95% confidence means the model is very sure about its classification; 55% means it's less certain (possibly ambiguous text).

Why it matters: Helps you decide whether to trust automated results or review manually. Low-confidence results might need human review.

3. Subjectivity vs Objectivity​

What it is: Whether text expresses a personal opinion (subjective) or states facts (objective).

Simple explanation:

  • Subjective: "This is the best restaurant ever!" (opinion)
  • Objective: "The restaurant is located on Silom Road." (fact)

Why it matters: Sentiment analysis only applies to subjective text. Objective statements don't have sentiment.

4. Sarcasm and Irony Detection​

What it is: The ability to recognize when someone says the opposite of what they mean.

Simple explanation: When someone writes "Oh great, another delay!" they don't actually think delays are great. Advanced sentiment analysis can detect this.

Challenge: This is one of the hardest problems in sentiment analysis, especially across languages and cultures.

5. Aspect (in Aspect-Based Sentiment Analysis)​

What it is: A specific feature, attribute, or component being discussed in a review or feedback.

Simple explanation: In a phone review, aspects might include: battery life, camera quality, screen display, price, and performance. Each aspect can have different sentiment.

Example: "Battery lasts all day (positive), but the camera is disappointing (negative)."

Why Sentiment Analysis is Important​

1. Scale Customer Understanding​

Problem: Businesses receive thousands of reviews, comments, and feedback daily Solution: Sentiment analysis processes everything instantly, giving you actionable insights

Real impact:

  • Process 10,000+ reviews in seconds
  • No more manual reading and categorization
  • Consistent, unbiased classification

2. Real-Time Brand Monitoring​

Problem: A PR crisis can spiral out of control before you notice Solution: Monitor sentiment in real-time to catch issues early

Real impact:

  • Detect sudden sentiment shifts immediately
  • Respond to negative trends before they escalate
  • Track brand perception over time

3. Product Development Insights​

Problem: Understanding what customers actually want is difficult Solution: Analyze feedback to identify common pain points and desires

Real impact:

  • Discover feature requests hidden in reviews
  • Prioritize improvements based on sentiment data
  • Validate product decisions with real customer opinion

4. Competitive Intelligence​

Problem: Hard to know how you compare to competitors Solution: Analyze sentiment across your industry

Real impact:

  • Benchmark your sentiment against competitors
  • Identify competitors' weaknesses to exploit
  • Learn from what customers love about others

5. Customer Service Optimization​

Problem: Hard to prioritize support tickets and identify angry customers Solution: Auto-tag and prioritize based on detected sentiment

Real impact:

  • Route angry customers to senior support
  • Identify at-risk customers before they churn
  • Measure support team performance by outcome sentiment

What Problems Does Sentiment Analysis Solve?​

Business ChallengeTraditional ApproachSentiment Analysis Solution
Review overloadManual reading (impossible at scale)Automatic classification in seconds
Brand crisis detectionReactive (see problems too late)Real-time monitoring and alerts
Customer feedback analysisSpreadsheets, guessworkData-driven insights and trends
Product prioritizationGut feelingEvidence-based decision making
Support ticket routingFirst-come-first-servePriority by urgency and emotion
Market researchExpensive surveysFree data from social media

Sentiment Analysis in Thailand: Real Applications​

1. E-Commerce Review Analysis​

Use case: Online marketplaces like Shopee, Lazada, JD Central

How it works:

  • Analyze millions of Thai product reviews
  • Automatically flag problematic sellers
  • Surface trending positive/negative feedback
  • Help buyers make informed decisions

Example with iApp API:

import requests

# Analyze Thai product review
response = requests.post(
    'https://api.iapp.co.th/v3/store/nlp/sentiment-analysis',
    headers={'apikey': 'YOUR_API_KEY'},
    json={'text': 'สินค้าดีมาก ส่งเร็ว แพ็คดี ถูกใจมาก'}
)

result = response.json()
# Output: {"label": "pos", "score": 0.92}

2. Social Media Monitoring for Thai Brands​

Use case: Banks, telcos, consumer brands monitoring Facebook, Twitter, Pantip

How it works:

  • Track brand mentions across Thai social media
  • Detect sentiment trends in real-time
  • Alert marketing team to viral negative posts
  • Measure campaign effectiveness

Thai-specific challenges solved:

  • Understanding Thai informal language and slang
  • Processing Thai script without spaces
  • Recognizing Thai-specific expressions ("555" = laughter, "จ้า" = friendly particle)

3. Customer Service for Thai Call Centers​

Use case: Banks, insurance companies, government services

How it works:

  • Analyze chat transcripts and emails
  • Auto-classify ticket urgency by sentiment
  • Identify frustrated customers for immediate callback
  • Track customer satisfaction trends

4. Political and Public Opinion Analysis​

Use case: Government agencies, NGOs, research organizations

How it works:

  • Analyze public sentiment on policies
  • Track opinion changes over time
  • Identify concerns and complaints
  • Inform policy decisions

5. Hotel and Tourism Reviews​

Use case: Hotels, airlines, tourism boards (TAT)

How it works:

  • Analyze reviews from Agoda, Booking.com, TripAdvisor
  • Identify common complaints (cleanliness, service, etc.)
  • Track sentiment by season or event
  • Benchmark against competitors

How to Use iApp Thai Sentiment Analysis API​

iApp Technology provides a production-ready Thai Sentiment Analysis API that understands Thai language nuances.

Quick Start​

1. Get Your API Key Visit API Key Management to get your free API key.

2. Make Your First Request

curl -X POST 'https://api.iapp.co.th/v3/store/nlp/sentiment-analysis' \
  -H 'apikey: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{"text": "อาหารอร่อยมาก บริการดีเยี่ยม"}'

3. Get Results

{
    "label": "pos",
    "score": 0.89
}

Python Example: Batch Analysis​

import requests

def analyze_sentiment(text, api_key):
    """Analyze sentiment of Thai text using iApp API"""
    response = requests.post(
        'https://api.iapp.co.th/v3/store/nlp/sentiment-analysis',
        headers={
            'apikey': api_key,
            'Content-Type': 'application/json'
        },
        json={'text': text}
    )
    return response.json()

# Analyze multiple reviews
reviews = [
    "สินค้าดี คุ้มค่า แนะนำเลย",
    "ไม่ค่อยพอใจ รอนานมาก",
    "ปกติ ไม่ได้แย่ ไม่ได้ดี"
]

api_key = 'YOUR_API_KEY'

for review in reviews:
    result = analyze_sentiment(review, api_key)
    sentiment = {
        'pos': 'Positive ✅',
        'neg': 'Negative ❌',
        'neu': 'Neutral ➖'
    }.get(result['label'], 'Unknown')

    print(f"Review: {review}")
    print(f"Sentiment: {sentiment} (Confidence: {result['score']:.1%})")
    print("---")

Output:

Review: สินค้าดี คุ้มค่า แนะนำเลย
Sentiment: Positive ✅ (Confidence: 91.2%)
---
Review: ไม่ค่อยพอใจ รอนานมาก
Sentiment: Negative ❌ (Confidence: 87.5%)
---
Review: ปกติ ไม่ได้แย่ ไม่ได้ดี
Sentiment: Neutral ➖ (Confidence: 78.3%)
---

JavaScript Example​

async function analyzeSentiment(text, apiKey) {
    const response = await fetch(
        'https://api.iapp.co.th/v3/store/nlp/sentiment-analysis',
        {
            method: 'POST',
            headers: {
                'apikey': apiKey,
                'Content-Type': 'application/json'
            },
            body: JSON.stringify({ text })
        }
    );
    return response.json();
}

// Usage
const result = await analyzeSentiment(
    'บริการประทับใจมาก พนักงานใจดี',
    'YOUR_API_KEY'
);
console.log(result);
// { label: 'pos', score: 0.94 }

Getting Started with Sentiment Analysis​

For Business Users​

  1. Identify your data sources: Where does customer feedback come from? (Reviews, social media, support tickets, surveys)
  2. Choose the right tool: Try iApp Thai Sentiment Analysis
  3. Start small: Analyze a sample of recent feedback to understand patterns
  4. Scale up: Automate analysis of all incoming feedback
  5. Take action: Use insights to improve products and services

For Developers​

  1. Get API access: Sign up for free API key
  2. Read the docs: Thai Sentiment Analysis API Documentation
  3. Test with sample data: Use the interactive demo
  4. Integrate: Add sentiment analysis to your application
  5. Monitor: Track API usage and results

Resources​

  1. Try the Demo: Thai Sentiment Analysis Demo
  2. API Documentation: Sentiment Analysis API
  3. Get API Key: API Key Management
  4. Related APIs: Toxicity Classification, Text Summarization
  5. Join Community: Discord

The Future of Sentiment Analysis​

  1. Multimodal Sentiment: Analyzing sentiment from text, voice, images, and video together
  2. Real-time Analysis: Instant sentiment detection for live streams and conversations
  3. Fine-grained Emotions: Beyond positive/negative to detect specific emotions with high accuracy
  4. Better Sarcasm Detection: AI models improving at understanding context and irony
  5. Privacy-Preserving Analysis: On-device processing for sensitive data

Why Thai Businesses Should Start Now​

  • Competitive advantage: Early adopters gain customer insights others miss
  • Cost efficiency: Automate what would take humans weeks to do manually
  • Customer retention: Identify and address unhappy customers before they leave
  • Data-driven culture: Build decisions on evidence, not assumptions
  • Thai language support: Use AI that truly understands Thai (not just translated English models)

Conclusion​

Sentiment Analysis transforms the overwhelming flood of customer opinions into actionable insights. Instead of drowning in thousands of reviews and comments, you can instantly understand what customers feel about your brand, products, and services.

For Thai businesses, having a sentiment analysis solution that truly understands Thai language nuances — the informal expressions, the cultural context, the unique writing style without spaces — is essential. iApp's Thai Sentiment Analysis API is built specifically for Thai text, trained on Thai data, and optimized for Thai business needs.

Ready to understand what your customers really think? Sign up for free and start analyzing Thai text sentiment today!

Questions? Join our Discord Community or email us at support@iapp.co.th.

iApp Technology Co., Ltd. Thailand's Leading AI Technology Company

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