Sentiment analysis (SA) is the process of evaluating human emotions, opinions, and reviews expressed in text to detect the writer’s mental outlook toward a particular event, topic, product, service, etc., and assign a relevant sentiment. In SA, to highlight the correct words which contribute toward sentiment cognition is very difficult. Simulating this task of shortlisting of words by human observers is challenging due to complexity of human mind’s processing. The use of fuzzy entropy is proposed in this article as an innovative step to tap sentiment quotients of online movie reviews. We have proposed a novel approach of shortlisting of words that help in sentiment cognition using a combination of fuzzy entropy, $k$ -means clustering, and sentiment lexicon SentiWordNet. We have addressed this challenging task of simulating the human cognition of words by developing a model that recognizes sentiment based on fuzzy scores derived from SentiWordNet in an automatic manner. Experiments on two benchmark movie review data sets—IMDB and polarity data sets by Pang and Lee—with training by long short-term memory neural networks, yields high accuracy for our approach as compared to other state-of-the-art-methods of SA.

中文翻译：

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模糊熵入围词的情感认知

情感分析（SA）是评估文本中表达的人类情感、观点和评论的过程，以检测作者对特定事件、主题、产品、服务等的心理看法，并分配相关的情感。在 SA 中，要突出对情感认知有贡献的正确词是非常困难的。由于人类思维处理的复杂性，模拟人类观察者筛选单词的任务具有挑战性。本文提出使用模糊熵作为挖掘在线电影评论情绪商的创新步骤。我们提出了一种新的词候选方法，它使用模糊熵、$k$ -means 聚类和情感词典 SentiWordNet 的组合来帮助情感认知。我们开发了一种模型，该模型基于从 SentiWordNet 派生的模糊分数以自动方式识别情感，从而解决了模拟人类对单词认知这一具有挑战性的任务。在两个基准电影评论数据集（IMDB 和 Pang 和 Lee 的极性数据集）上进行的实验，通过长短期记忆神经网络进行训练，与其他最先进的方法相比，我们的方法产生了更高的准确性萨。