Lexicon-Based Sentiment Analysis
Also known as: dictionary-based sentiment analysis, rule-based sentiment scoring, Sözlük Tabanlı Duygu Analizi
Lexicon-based sentiment analysis computes sentiment at the word level using prebuilt sentiment dictionaries such as AFINN (Nielsen, 2011), SentiWordNet, VADER (Hutto & Gilbert, 2014), and the NRC Emotion Lexicon. It scores text by looking words up in a dictionary of charged terms, so it requires no labelled training data.
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When to use it
Use lexicon-based sentiment analysis when you have text data and a sentiment dictionary that matches its language, but no labelled training set to fit a supervised model. It suits classification and descriptive goals on cross-sectional or longitudinal text and needs only a small corpus (around 10 documents or more). It is the natural choice when collecting labelled data is impractical, since it requires none.
Strengths & limitations
- Requires no labelled training data — the prebuilt dictionary supplies the scores directly.
- Simple, fast, and transparent: every score traces back to specific words in the lexicon.
- Works on small corpora and is easy to apply with established lexicons such as AFINN and VADER.
- Depends entirely on a sentiment dictionary existing for the target language.
- Text preprocessing is mandatory; without it the lexicon misses or mismatches words.
- Words absent from the dictionary contribute nothing, so domain-specific or novel vocabulary is ignored.
Frequently asked
Does lexicon-based sentiment analysis need training data?
No. It uses a prebuilt sentiment dictionary such as AFINN, SentiWordNet, VADER, or the NRC Emotion Lexicon and computes scores by word lookup, so no labelled training set is required.
Which lexicon should I choose?
Pick a dictionary that matches your corpus language and text type. AFINN and VADER are tuned for short, social-media style text, SentiWordNet draws on WordNet senses, and the NRC Emotion Lexicon adds emotion categories beyond polarity. The lexicon must cover the language of your text.
Why is preprocessing required?
Scores come from matching tokens against the dictionary, so the text must be normalised and tokenised first. Without preprocessing, noisy or non-standard tokens fail to match the lexicon and the sentiment reading degrades.
How does it differ from machine-learning sentiment analysis?
A lexicon-based method scores words with a fixed dictionary and learns nothing from data, while a machine-learning approach trains a classifier on labelled examples and adapts to the domain. The lexicon route is simpler and needs no labels, but cannot learn domain-specific patterns or handle context such as sarcasm.
Sources
- Nielsen, F.Å. (2011). A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs. Proceedings of the ESWC Workshop on 'Making Sense of Microposts'. link ↗
- Hutto, C.J. & Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 8(1), 216-225. DOI: 10.1609/icwsm.v8i1.14550 ↗
How to cite this page
ScholarGate. (2026, June 1). Lexicon-Based Sentiment Analysis. ScholarGate. https://scholargate.app/en/text-mining/lexicon-based-sentiment
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.
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- Subjectivity DetectionText mining↔ compare
- Text Complexity AnalysisText mining↔ compare