Sentiment Analysis

Sentiment Analysis Services

Definition

Sentiment analysis is the process of determining the emotional tone (positive, negative, neutral) in text data. It is an NLP task used in social media monitoring, customer feedback, and market analysis.

Purpose

The purpose is to understand public opinion, customer satisfaction, and social trends automatically at scale.

Importance

  • Helps businesses track brand reputation.
  • Supports political and social science research.
  • Risk of misclassification due to sarcasm or ambiguity.
  • Related to text classification tasks.

How It Works

  1. Collect and preprocess text data.
  2. Label data with sentiment categories.
  3. Train ML models using supervised or unsupervised learning.
  4. Apply models to new text inputs.
  5. Aggregate and analyze sentiment trends.

Examples (Real World)

  • Twitter sentiment analysis during elections.
  • Amazon reviews analyzed for product improvement.
  • Financial firms tracking sentiment for stock predictions.

References / Further Reading

  • Pang & Lee. “Opinion Mining and Sentiment Analysis.” Foundations and Trends in Information Retrieval.
  • Jurafsky & Martin. Speech and Language Processing.
  • IEEE Transactions on Affective Computing.