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独特地评估文本的词汇结构，从而展示句子中否定检测的方法。此外，所提出的方法考察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。流行的表达检测器应用跳过了这一重要步骤，从而忽略了被否定网络捕获的根词，使得机器学习和情感分析的文本分类变得困难。本研究采用自然语言处理（NLP）方法，发现并反义词化被否定的词语，以提高文本分类的准确性。该方法作为一个透镜，通过名为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。初步结果表明，我们的初始分析改进了传统情感分析，后者有时会忽略词语否定或分配相反的极性分数。SentiWordNet分析器提高了35%，Vader分析器提高了20%，TextBlob分析器提高了6%。