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:此项研究的目的是通过单词思维脱节,通过对文本的词汇结构进行独特的评估,展示在句子中发现否定现象的方法。 拟议的框架审查了文本中各种表达方式中的所有独特特征,以解决所有符号的背景使用情况,并破译否定对情绪分析的影响。 流行表达探测器的运用跳过这一重要步骤,从而忽略了否定网络中的根字,使文本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,利用名为WordHoard的NLP图书馆提供的知识库,发现和安眠被否定的文字,以便提高文本分类的准确性。 早期结果显示,我们对传统情绪分析的初步分析有所改进,有时忽视否定现象或赋予反极分数。 SentiWordNet 分析器改进了35%, Vader 分析器改进了20%, TextBlob 改进了6%。