Metadata-Version: 2.4
Name: nltk-vader-sentiment
Version: 1.0.0
Summary: A modern, dependency-free reimplementation of the VADER sentiment analyzer (Hutto & Gilbert, 2014), with the sentiment and emoji lexicons bundled as data — no downloads, no pickles, no file parsing at runtime.
Project-URL: Homepage, https://github.com/alvations/nltk_vader
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Project-URL: Issues, https://github.com/alvations/nltk_vader/issues
Project-URL: Changelog, https://github.com/alvations/nltk_vader/blob/main/CHANGELOG.md
Author-email: alvations <alvations@gmail.com>
Maintainer-email: alvations <alvations@gmail.com>
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Keywords: lexicon,nlp,nltk,opinion-mining,rule-based,sentiment,sentiment-analysis,social-media,vader
Classifier: Development Status :: 5 - Production/Stable
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Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
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Description-Content-Type: text/markdown

# nltk-vader-sentiment

A modern, dependency-free reimplementation of the **VADER** sentiment
analyzer (Hutto & Gilbert, ICWSM-14) — with the sentiment and emoji lexicons
bundled as Python data. No downloads, no pickles, no file parsing at
runtime, no dependencies, and **no `nltk` import anywhere**.

```bash
pip install nltk-vader-sentiment
```

```python
>>> from vader import polarity_scores
>>> polarity_scores("VADER is smart, handsome, and funny!")
{'neg': 0.0, 'neu': 0.248, 'pos': 0.752, 'compound': 0.8439}

>>> from vader import SentimentIntensityAnalyzer
>>> analyzer = SentimentIntensityAnalyzer()          # reuse this; it is thread-safe
>>> analyzer.polarity_scores("Not bad at all :) ❤️")
{'neg': 0.0, 'neu': 0.285, 'pos': 0.715, 'compound': 0.8765}
```

There is also a CLI:

```bash
$ vader "This is amazingly good!"
{"neg": 0.0, "neu": 0.463, "pos": 0.537, "compound": 0.54}
$ vader < reviews.txt        # one JSON object per line
```

## Why this package

The two maintained homes of VADER have both drifted and stalled:

* **[cjhutto/vaderSentiment](https://github.com/cjhutto/vaderSentiment)** —
  the original. Its last PyPI release (3.3.2) is from 2020, the repository
  is essentially dormant, it installs `requests` for a demo block, and its
  issue tracker documents years of unfixed scoring bugs.
* **[nltk.sentiment.vader](https://www.nltk.org/api/nltk.sentiment.vader.html)** —
  a 2016-era snapshot that has drifted from upstream
  ([nltk#2592](https://github.com/nltk/nltk/issues/2592)): no emoji
  handling, a different tokenizer that misses `(super)` and `word!!`
  ([nltk#3071](https://github.com/nltk/nltk/issues/3071)), and a
  `vader_lexicon` that must be fetched with `nltk.download()` at runtime.

This package reimplements the algorithm from scratch against the upstream
reference, fixes the catalogued defects (each fix carries a regression test
naming its issue), and ships everything needed at `pip install` time. It is
built to the same standard as
[`nltk-punkt-tokenize`](https://pypi.org/project/nltk-punkt-tokenize/), so
that NLTK could one day vendor sentiment analysis out to it — which is why
nothing here may ever import `nltk` (enforced by tests three ways).

## Parity with upstream

Correctness is measured, not claimed. Against a pinned snapshot of upstream
`vaderSentiment` on 1,219 cases (the upstream demo sentences plus samples
from all four ICWSM-14 ground-truth datasets):

* **1,189/1,219 (97.5%) identical compound scores.** Every one of the 30
  divergences is traced to a deliberate bug fix (see below).
* Correlation with the ICWSM-14 **human ground truth** matches upstream to
  within ±0.0003 on all four datasets:

| dataset | n | upstream *r* | ours *r* |
|---|---|---|---|
| tweets | 4,200 | 0.8722 | 0.8719 |
| movie reviews | 10,605 | 0.4276 | 0.4273 |
| Amazon reviews | 3,708 | 0.5896 | 0.5893 |
| NYT editorials | 5,190 | 0.5029 | 0.5031 |

## Fixed defects

Engine fixes (relative to upstream master and/or NLTK):

| fix | reference |
|---|---|
| Repeated tokens scored with the first occurrence's negation/booster context | [nltk#3638](https://github.com/nltk/nltk/pull/3638) semantics |
| O(n²) token scan (CPU-DoS on long text) and O(n²) re-lowercasing per rule | [nltk#3638](https://github.com/nltk/nltk/pull/3638), [vaderSentiment#110](https://github.com/cjhutto/vaderSentiment/issues/110) |
| Tokenizer strips only one punctuation char/side, so `(super)`, `good!!!!` miss the lexicon | [nltk#3071](https://github.com/nltk/nltk/issues/3071) |
| Hashtags scored as neutral (`#bad`) | [nltk#2637](https://github.com/nltk/nltk/issues/2637) |
| `ok!`, `No,` and other short tokens kept with punctuation, missing the lexicon | upstream `<=2 chars` rule |
| Emoticons with trailing punctuation (`:),`) never match | — |
| Typographic apostrophes: `don’t` not recognised as negation | [vaderSentiment#66](https://github.com/cjhutto/vaderSentiment/issues/66) |
| Multi-code-point emoji (`❤️`, ZWJ sequences, skin tones) never match | [vaderSentiment#117](https://github.com/cjhutto/vaderSentiment/issues/117), [#99](https://github.com/cjhutto/vaderSentiment/issues/99) |
| Emoji description fused with following word | [vaderSentiment#86](https://github.com/cjhutto/vaderSentiment/issues/86) |
| `_but_check` scales the first value-equal element; only deterministic since 2020 | [nltk#2581](https://github.com/nltk/nltk/issues/2581) |
| "never so/this" ×1.25 boost fires without any "never" (operator precedence) | found in source; see `tests/test_known_issues.py` |
| ALLCAPS emphasis applied to zero-valence words | [vaderSentiment#130](https://github.com/cjhutto/vaderSentiment/issues/130) |
| `IndexError` when a sentence ends with "no" (unguarded builds) | [vaderSentiment#146](https://github.com/cjhutto/vaderSentiment/issues/146) |
| `negated()` matches "n't" anywhere in a token, not as a suffix | — |
| Special-case idioms mostly unreachable ("to die for", "bus stop") | [vaderSentiment#144](https://github.com/cjhutto/vaderSentiment/issues/144) |
| Sentiment-laden idioms ("break a leg") declared future work, never wired | [vaderSentiment#144](https://github.com/cjhutto/vaderSentiment/issues/144) |
| Multiword lexicon entries ("can't stand", "fed up", "screwed up") dead weight | [vaderSentiment#124](https://github.com/cjhutto/vaderSentiment/issues/124) |
| Non-`str` input coerced through `str()`/broken bytes branch instead of raising | [vaderSentiment#107](https://github.com/cjhutto/vaderSentiment/issues/107) |

Lexicon fixes (resolved at build time by `tools/build_data.py`, every
resolution logged):

* 14 duplicate keys with conflicting scores → upstream's effective
  (last-wins) value kept for parity
  ([vaderSentiment#122](https://github.com/cjhutto/vaderSentiment/issues/122));
  the two **sign-flipped** emoticons `d:`/`d=` additionally get the mean
  over both rating rounds via the curated layer.
* 14 uppercase keys were unreachable (lookups lowercase the token): keys are
  case-folded, keeping the previously-reachable value on collision; `:Þ` and
  `:-Þ`, which had no lowercase twin and were dead data, now work.
* Curated additions ([details](src/vader/data/curated.py)): `kindly` → 0.0
  ([vaderSentiment#155](https://github.com/cjhutto/vaderSentiment/issues/155));
  the `slaughter*` family, absent while `killed`/`murdered` score −3.5/−3.4
  ([nltk#3507](https://github.com/nltk/nltk/issues/3507)). Disable all
  curated changes with `SentimentIntensityAnalyzer(curated=False)`.

## Customisation

Every word list is per-instance constructor data — no module-global
mutation ([vaderSentiment#102](https://github.com/cjhutto/vaderSentiment/issues/102),
[#105](https://github.com/cjhutto/vaderSentiment/issues/105),
[#111](https://github.com/cjhutto/vaderSentiment/issues/111)):

```python
analyzer = SentimentIntensityAnalyzer(
    extra_lexicon={"rugpull": -2.9},   # domain terms on top of the default
    negations=[...],                   # replace the negation list
    booster={...},                     # replace degree adverbs
    special_cases={...},               # phrase overrides
    idioms={},                         # {} disables sentiment-laden idioms
    alpha=15.0,                        # compound normalisation constant
)
```

Instances are immutable after construction and safe to share across threads.

## Design and security

Same rules as `nltk-punkt-tokenize`:

* **Nothing imports `nltk`** — not the library, not the tests. Enforced by
  AST scan, by a child interpreter where `import nltk` raises, and by an
  empty dependency list. The intended dependency arrow is
  `nltk → nltk-vader-sentiment`, never a cycle back.
* **Zero dependencies, stdlib only.** The upstream package installs
  `requests` for a demo; this one installs nothing.
* **Lexicons are code-like data**: generated Python literal modules under
  `vader/data/`, imported, never parsed. The generator re-parses its own
  output and rejects any AST node that is not literal data.
* **No file parser ships**: `tools/` (the only code that reads lexicon
  files) is excluded from both the wheel and the sdist. Get it from the
  repository, deliberately.
* No pickles, no downloader, no network, no `eval`, no runtime file IO —
  all test-enforced.

## Known limitations (documented, not "fixed")

Inherent to a lexicon-and-rules model — if these matter, use a trained model:

* **Sarcasm/irony**: "The movie was funny... not" stays positive.
* **Compound saturation**: `compound` is `sum/√(sum²+α)`, so long texts
  saturate towards ±1 and scores are not comparable across lengths
  ([vaderSentiment#151](https://github.com/cjhutto/vaderSentiment/issues/151)).
  Score per sentence and aggregate.
* **Domain sensitivity**: tuned for social media; weaker on finance, health
  and news (SentiBench; use `extra_lexicon` for domain terms).
* **English only.** Do not feed it other languages or machine translations.
* **Lexicon bias**: published audits find disability-related terms skew
  negative and queer identity terms are absent (Ungless et al. 2023;
  TrustNLP 2023). The curated layer fixes only clear-cut cases; audit
  against your own data before making decisions about people.

## Development

```bash
pip install -e ".[dev]"
pytest                  # package suite (runs without tools/, as in an sdist)
pytest tools/tests      # maintainer suite for the data converters
ruff check src tests tools && mypy
```

Regenerate the data modules from an upstream checkout:

```bash
python tools/build_data.py /path/to/vaderSentiment/vaderSentiment
python tools/generate_parity_fixtures.py /path/to/vaderSentiment
```

## Citation and license

Licensed under the **Apache License 2.0** (see `LICENSE`). The VADER
algorithm and lexicons are by C.J. Hutto, MIT-licensed (see `NOTICE`). If
you use VADER, please cite:

> Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model
> for Sentiment Analysis of Social Media Text. Eighth International
> Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June
> 2014.
