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%.

翻译：本研究旨在通过词义消歧唯一性地评估文本的词汇结构，展示句子中否定检测的方法。此外，所提出的方法检查了文本中相关表达的所有独特特征，以解析句子的上下文用法以及否定对情感分析的影响。流行的表达检测器的应用跳过了这一重要步骤，从而忽视了陷入否定网络中的根词，使得机器学习与情感分析中的文本分类变得困难。本研究采用自然语言处理（NLP）方法发现并反义化被否定的词语，以提高文本分类的准确性。该方法如同一个透镜，通过由名为WordHoard的NLP库提供的知识库读取给定的词序列，以检测否定信号。初步结果显示，我们的初始分析改进了传统的有时忽略词否定或分配相反极性分数的基础情感分析。SentiWordNet分析器性能提升了35%，Vader分析器提升了20%，TextBlob分析器提升了6%。