Features and Updates

Many thanks to George Berry, Ewan Klein, Pierpaolo Pantone for key contributions to make VADER better. The new updates includes capabilities regarding:

  1. Refactoring for Python 3 compatibility, improved modularity, and incorporation into [NLTK] …many thanks to Ewan & Pierpaolo.

  2. Restructuring for much improved speed/performance, reducing the time complexity from something like O(N^4) to O(N)…many thanks to George.

  3. Simplified pip install and better support for vaderSentiment module and component import. (Dependency on vader_lexicon.txt file now uses automated file location discovery so you don’t need to manually designate its location in the code, or copy the file into your executing code’s directory.)

  4. More complete demo in the __main__ for vaderSentiment.py. The demo has:

    • examples of typical use cases for sentiment analysis, including proper handling of sentences with:

      • typical negations (e.g., “not good”)

      • use of contractions as negations (e.g., “wasn’t very good”)

      • conventional use of punctuation to signal increased sentiment intensity (e.g., “Good!!!”)

      • conventional use of word-shape to signal emphasis (e.g., using ALL CAPS for words/phrases)

      • using degree modifiers to alter sentiment intensity (e.g., intensity boosters such as “very” and intensity dampeners such as “kind of”)

      • understanding many sentiment-laden slang words (e.g., ‘sux’)

      • understanding many sentiment-laden slang words as modifiers such as ‘uber’ or ‘friggin’ or ‘kinda’

      • understanding many sentiment-laden emoticons such as :) and :D

      • understanding sentiment-laden initialisms and acronyms (for example: ‘lol’)

    • more examples of tricky sentences that confuse other sentiment analysis tools

    • example for how VADER can work in conjunction with NLTK to do sentiment analysis on longer texts…i.e., decomposing paragraphs, articles/reports/publications, or novels into sentence-level analyses

    • examples of a concept for assessing the sentiment of images, video, or other tagged multimedia content

    • if you have access to the Internet, the demo has an example of how VADER can work with analyzing sentiment of texts in other languages (non-English text sentences).