Registry / ai-ml / vadersentiment

vadersentiment

library3.3.2pypypi✓ verified 26d ago

VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool, specifically attuned to sentiments expressed in social media, and effective on texts from other domains. The current version is 3.3.2, with releases occurring infrequently as it is a mature, rule-based system.

ai-ml

pip install vadersentiment

github ↗package ↗

INSTALL

IMPORT

SIG · VADERSENTIMENT

vadersentiment

ai-mlpythonv3.3.2

Install

2.1s avg

Import

33ms

Disk

20MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v3.3.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.034s · 21.7MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 2.1s · import 0.032s · 22MB

20MB installed

● package 20MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

SentimentIntensityAnalyzer

✓ from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

✗ from nltk.sentiment.vader import SentimentIntensityAnalyzer

While NLTK integrates VADER, importing directly from `nltk.sentiment.vader` uses NLTK's packaged version, which may differ slightly or require `nltk.download('vader_lexicon')`. The `vadersentiment` package includes its lexicon internally, so the correct import for this standalone package is from `vaderSentiment.vaderSentiment`.

Initialize the SentimentIntensityAnalyzer and use the `polarity_scores()` method to get sentiment scores for a given text. The output is a dictionary containing negative ('neg'), neutral ('neu'), positive ('pos'), and a compound score ('compound').

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer analyser = SentimentIntensityAnalyzer() sentence = "VADER sentiment analysis is incredibly insightful and super fun!" scores = analyser.polarity_scores(sentence) print(scores) sentence_negative = "This product is absolutely terrible and a complete waste of money." scores_negative = analyser.polarity_scores(sentence_negative) print(scores_negative)

Debug

Known issues

gotchaVADER's primary output, the 'compound' score, is normalized between -1 (most extreme negative) and +1 (most extreme positive). Common thresholds for interpretation are: positive sentiment if `compound >= 0.05`, negative sentiment if `compound <= -0.05`, and neutral sentiment otherwise.

fix

Always check the `compound` score for overall sentiment, and understand its typical interpretation thresholds. The 'neg', 'neu', 'pos' scores represent the proportion of text that falls into each category.

affects: All versions

gotchaVADER is highly sensitive to capitalization, punctuation (e.g., exclamation marks), and degree modifiers (e.g., 'very', 'kind of'). While a feature, this can lead to unexpected results if text is preprocessed heavily without considering these aspects.

fix

Be mindful of text preprocessing steps. Avoid over-aggressively stripping punctuation or normalizing case if you want to leverage VADER's sensitivity to these sentiment indicators.

affects: All versions

gotchaVADER, being a rule-based system with a static lexicon, can struggle with sarcasm, irony, subtle or complex negations ('not bad' can still lean positive), and evolving slang or domain-specific terminology that isn't in its lexicon. It might also struggle with intricate sentence structures involving conjunctions like 'but' that shift sentiment.

fix

For highly nuanced or domain-specific sentiment analysis, consider supplementing VADER with lexicon expansion techniques (e.g., using word embeddings) or machine learning models that are trained on relevant data. For 'but' clauses, be aware that VADER attempts to prioritize the sentiment after 'but'.

affects: All versions

breakingPrior to version 1.0 (specifically earlier versions, e.g., pre-3.x), users sometimes needed to manually ensure the `vader_lexicon.txt` file was accessible or manage NLTK downloads. This could lead to `FileNotFoundError` or import issues.

fix

Ensure you are using `vadersentiment` version 3.0 or higher. The current versions (e.g., 3.3.2) handle lexicon discovery automatically when importing `SentimentIntensityAnalyzer` directly from `vaderSentiment.vaderSentiment`.

affects: < 3.0 (approx)

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'vaderSentiment'

The `vadersentiment` library is not installed in the current Python environment, or the import statement uses incorrect capitalization for the top-level module.

fix

First, install the package: `pip install vadersentiment`. Then ensure the import path is correct, e.g., `from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer`.

AttributeError: module 'vaderSentiment' has no attribute 'SentimentIntensityAnalyzer'

The `SentimentIntensityAnalyzer` class is not directly exposed by the top-level `vaderSentiment` module; it is nested within another module of the same name inside the package.

fix

Correct the import statement to specify the full path: `from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer`

NameError: name 'SentimentIntensityAnalyzer' is not defined

The `SentimentIntensityAnalyzer` class was used without being correctly imported or instantiated in the current scope.

fix

Ensure that the class is imported correctly with `from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer` and then instantiate it, e.g., `analyzer = SentimentIntensityAnalyzer()`.

Upgrade

Version history

3.3.2latest on PyPI · released May 22, 2020

Audit

Dependencies

No dependency data recorded yet.

Agent activity

20 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/cjhutto/vaderSentiment ↗

packagehttps://pypi.org/project/vadersentiment/ ↗