Sentiment Analysis — Opinion Mining
Sentiment Analysis (Opinion Mining) · Also known as: opinion mining, polarity detection, duygu analizi
Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.
Read the full method
Sign in with a free account to read this section.
Method map
The neighbourhood of related methods — select a node to explore.
+58 more
When to use it
Sentiment analysis fits when you have text data to work with and a language model or lexicon that matches the language of that text. A small corpus can be handled with a simple lexicon, while larger labelled corpora support supervised classifiers or transformers. Supervised models in particular need a reasonably large, balanced labelled set; with very few documents a lexicon-based method is the safer choice. If there is no text data at all, sentiment analysis cannot run.
Strengths & limitations
- Flexible across approaches: a simple lexicon works on small corpora, while transformers capture context in large ones.
- Turns unstructured opinion text into structured, quantifiable polarity signals.
- Scales from quick dictionary scoring to context-aware language models without changing the overall pipeline.
- Supervised models need a sufficiently large, balanced labelled corpus to learn reliable patterns.
- Performance depends on the language model or lexicon matching the corpus language.
- Sarcasm, negation, and domain shift remain hard for every approach.
Frequently asked
Lexicon, machine learning, or transformer — which approach should I use?
A lexicon-based approach applies a dictionary of sentiment-bearing words and works well on small corpora with no labelled data. A machine-learning classifier learns patterns from a labelled training set and adapts to your domain. A transformer-based model uses a pretrained language model to capture context and generally performs best on large corpora, at higher computational cost.
How accurate is sentiment analysis?
Accuracy varies with the approach, the corpus, and how well the model matches the text. Evaluate with accuracy, F1, precision, and recall on held-out labelled data; on imbalanced classes, accuracy alone can be misleading, so rely on F1, precision, and recall.
Can it work in languages other than English?
Yes, but the chosen language model or lexicon must match the target language of the corpus. Applying an English lexicon or model to text in another language is a common cause of poor results.
How are negation and sarcasm handled?
These remain among the hardest cases. Negation can flip polarity and sarcasm can invert the literal meaning entirely. Context-aware transformer models cope better than simple lexicons, but no approach handles sarcasm reliably, so error inspection during evaluation matters.
Sources
- Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI: 10.1561/1500000011 ↗
How to cite this page
ScholarGate. (2026, June 1). Sentiment Analysis (Opinion Mining). ScholarGate. https://scholargate.app/en/text-mining/sentiment-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- BERT EmbeddingsText mining↔ compare
- Text ClassificationText mining↔ compare
- TF-IDFText mining↔ compare