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:这项研究的目的是通过单词感脱矛盾,通过对文本的词汇结构进行独特的评估,展示发现句子中否定现象的方法。此外,拟议方法审查了文本中相关表达的所有独特特征,以解决句子的背景使用和否定对情绪分析的影响。流行表达探测器的应用跳过这一重要步骤,从而忽略了否定网中的根字,从而忽略了否定网中的根字,使文字分类难以进行机器学习和情绪分析。本研究采用了自然语言处理(NLP)方法,以发现和安眠文字,这些文字被否定,以便提高文本分类的准确性。这种方法作为透视镜,利用一个名为WordHoard的NLP图书馆提供的知识库,通过一个特定文字序列读出一个特定文字序列,以检测否定信号。早期结果显示,我们的初步分析改进了传统情感分析,有时忽略了否定字或赋予反极分数。SentiWordNet分析器改进了35%,Vader分析器改进了20%,TextBlob分析器改进了6%。