vaderSentiment 3.3.2

VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media. It is fully open-sourced under the [MIT License] (we sincerely appreciate all attributions and readily accept most contributions, but please don’t hold us liable).

Citation Information

If you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Python code for rule-based sentiment analysis engine) in your research, please cite the above paper. For example:

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.

• Code examples are provided on the [VADER GitHub Repo]

• Details about the scoring are provided on the [VADER GitHub Repo]

• VADER has been ported to Java, JavaScript, PHP, Scala, C#, Rust, and Go (see details and links to these on the [VADER GitHub Repo]

Keywords: vader,sentiment,analysis,opinion,mining,nlp,text,data,text analysis,opinion analysis,sentiment analysis,text mining,twitter sentiment,opinion mining,social media,twitter,social,media Platform: any Classifier: Development Status :: 5 - Production/Stable Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: MIT License Classifier: Natural Language :: English Classifier: Programming Language :: Python :: 3.5 Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence Classifier: Topic :: Scientific/Engineering :: Information Analysis Classifier: Topic :: Text Processing :: Linguistic Classifier: Topic :: Text Processing :: General Description-Content-Type: text/x-rst