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%.

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，从而展示检测句子中否定的方法。所提出的框架考察文本中各种表达方式的所有独特特征，以解析所有标记的上下文用法，并解读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，从而忽略了处于否定网络中的词根，使得机器学习与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，借助名为WordHoard的NLP库提供的知识库，发现被否定的词汇并将其反义化，以提高文本分类的准确性。初步结果显示，我们的初始分析优于传统的情感分析，后者有时会忽略否定或分配相反的情感极性得分。SentiWordNet分析器性能提升35%，Vader分析器提升20%，TextBlob提升6%。