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. Additionally, the proposed method examined all the unique features of the related expressions within a text to resolve the contextual usage of the sentence and 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. This method acts as a lens that reads through a given word sequence using a knowledge base provided by an NLP library called WordHoard in order to detect negation signals. Early results show that our initial analysis improved traditional sentiment analysis that sometimes neglects word negations or assigns an inverse polarity score. The SentiWordNet analyzer was improved by 35%, the Vader analyzer by 20% and the TextBlob analyzer by 6%.
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