Sentiment Analyser using VADER Library

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. VADER uses a combination of A sentiment lexicon is a list of lexical features (e.g., words) which are generally labelled according to their semantic orientation as either positive or negative.

Advantages of using VADER

VADER has a lot of advantages over traditional methods of Sentiment Analysis, including:

• It works exceedingly well on social media type text, yet readily generalizes to multiple domains

• It doesn’t require any training data but is constructed from a generalizable, valence-based, human-curated gold standard sentiment lexicon

• It is fast enough to be used online with streaming data, and

• It does not severely suffer from a speed-performance tradeoff.

Citation Info

• 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.

  For more and detailed info: check the main repository https://github.com/cjhutto/vaderSentiment/

Installation

mainly two lib: vaderSentiment and requests

pip install -r requirement.txt

Usage

Run and check the example data sentiment:

python sentiment_analyzer.py

Meaning of Scores

• The Positive, Negative and Neutral scores represent the proportion of text that falls in these categories. This means our sentence was rated as 67% Positive, 33% Neutral and 0% Negative. Hence all these should add up to 1.

• The Compound score is a metric that calculates the sum of all the lexicon ratings which have been normalized between -1(most extreme negative) and +1 (most extreme positive). In the case above, lexicon ratings for and supercool are 2.9 and respectively 1.3. The compound score turns out to be 0.75 , denoting a very high positive sentiment.

  Compound matrix score

  1. Positive sentiment : compund score >= 0.05
  2. Neutral sentiment : compund score > -0.05 and compund score < 0.05
  3. Negative sentiment : compund score <= -0.05