Sentiment Analysis
Deciding whether text is positive or negative — the most deployed NLP task, and the one whose target may not exist.
Reviewed July 12, 2026Contested
When not to use it
- On individual high-stakes decisions. It's an aggregate instrument. Routing one customer on it is misusing the tool.
- When you needed aspect-level detail. "Negative" tells you nothing about what to fix. Aspect-based sentiment does.
- Across domains without checking. "Predictable" is negative for films and positive for delivery. The model only knows what it saw.
- On sarcasm-heavy text. It's unsolved, plausibly unsolvable from text alone, and humans aren't good at it either.
Reach for something else instead
- Aspect-based sentiment — sentiment per topic. Usually the thing you actually wanted.
- Direct measurement — churn, returns, NPS. If you can measure the behaviour, don't infer the feeling.
- Lexicon methods — instant, free, interpretable, and a fair baseline on social text.
- Emotion or stance classification — richer, with their own construct problems.
Further reading
- Pang & Lee (2008), Opinion Mining and Sentiment Analysis — the founding survey; still clear about what the task is and isn't.
- Socher et al. (2013), Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank — the benchmark, and where compositional sentiment got taken seriously.
- Hutto & Gilbert (2014), VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text — the lexicon baseline that keeps being competitive.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Treating a document-level score as actionable. It aggregates away the information you needed.
- Ignoring domain shift. A movie-review model on support tickets is measuring the wrong vocabulary.
- Reporting accuracy above the annotator agreement rate without noticing what that implies about the labels.
- Assuming sentiment is a property of text. It's a property of a reading, and readings differ.
- Using it on individuals rather than trends. It's a thermometer for a population, not a diagnosis for a person.
At a glance
FieldApplied AI
Worksaggregate trends on clear text
Failsindividuals, sarcasm, mixed opinions, domain shift
Real ceilingannotator agreement, often 70–80%
The upgradeaspect-based sentiment
DifficultyBeginner
Often compared with
Document-level vs. aspect-based sentiment — one number for the whole review vs. one per thing discussed. The first is easier to dashboard; the second is what you can act on.
Where this sits
A destination. 4 concepts lead here, and nothing in the corpus depends on it.
3Levelsteps in
4Needs firstconcepts
0Opens upnothing further
1Areastays here
Learn these firstText Classification
Computed from the prerequisite graph, not assigned. How this works