  This study aims to demonstrate the methods for detecting negations in a
sentence by uniquely evaluating the lexical structure of the text via
word-sense disambiguation. The proposed framework examines all the unique
features in the various expressions within a text to resolve the contextual
usage of all tokens and decipher the effect of negation on sentiment analysis.
The application of popular expression detectors skips this important step,
thereby neglecting the root words caught in the web of negation and making text
classification difficult for machine learning and sentiment analysis. This
study adopts the Natural Language Processing (NLP) approach to discover and
antonimize words that were negated for better accuracy in text classification
using a knowledge base provided by an NLP library called WordHoard. Early
results show that our initial analysis improved on traditional sentiment
analysis, which sometimes neglects negations or assigns an inverse polarity
score. The SentiWordNet analyzer was improved by 35%, the Vader analyzer by 20%
and the TextBlob by 6%.
