A Semantic Approach to Negation Detection and Word Disambiguation with Natural Language Processing

TL;DR

This paper presents a semantic NLP framework for negation detection and word disambiguation to improve sentiment analysis accuracy by addressing negation effects more effectively.

Contribution

It introduces a novel NLP-based method that uniquely evaluates lexical structures and disambiguates words to enhance sentiment analysis accuracy.

Findings

SentiWordNet accuracy improved by 35%

Vader analyzer improved by 20%

TextBlob improved by 6%

Abstract

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%.