Wang Xi, Nourmohammadi Samad
Yunnan Agricultural University, Puer, Yunnan, 665099, PR China.
Islamic Azad University, Tehran, Iran.
Sci Rep. 2025 May 16;15(1):17038. doi: 10.1038/s41598-025-01516-y.
Sentiment analysis of content is highly essential for myriad natural language processing tasks. Particularly, as the movies are often created on the basis of public opinions, reviews of people have gained much attention, and analyzing sentiments has also become a crucial and demanding task. The unique characteristics of this data, such as the length of text, spelling mistakes, and abbreviations, necessitate a non-conventional method and additional stages for sentiment analysis in such an environment. To do so, this paper conducted two different word embedding models, namely GloVe and Word2Vec, for vectorization. In this study, Bidirectional Gated Recurrent Unit was employed, since there were two polarities, including positive and negative. Then, it was optimized by the Enhanced Human Evolutionary Optimization (EHEO) algorithm, hence improving the hyperparameters. The findings showed that using GloVe, the Bi-GRU/EHEO model achieved 97.26% for precision, 96.37% for recall, 97.42% for accuracy, and 96.30% for F1-score. With Word2Vec, the suggested model attained 98.54% for precision, 97.75% for recall, 97.54% for accuracy, and 97.63% for F1-score. These model were compared with other models like GRU that accomplished the precision, recall, accuracy, and F1-score values of 89.24, 90.14, 89.57, and 89.68 for Glove as well the values of 89.67, 90.18, 90.75, and 89.41 for Word2Vec; and Bi-GRU that accomplished the values of 90.13, 90.47, 90.71, and 90.30 for Glove, as well as the values of 90.31, 90.76, 90.67, and 90.53 for Word2Vec. The suggested sentiment analysis approaches demonstrated much potential to be used in real-world applications, like customer feedback evaluation, political opinion analysis, and social media sentiment analysis. By using these models' high efficiency and accuracy, the approaches could have offered some practical solutions for diverse industries to forecast trends, enhance decision-making procedures, and examine textual data.
内容的情感分析对于众多自然语言处理任务至关重要。特别是，由于电影通常基于公众意见创作，人们的评论备受关注，情感分析也已成为一项关键且具有挑战性的任务。此类数据的独特特征，如文本长度、拼写错误和缩写，使得在这种环境下进行情感分析需要一种非常规方法和额外的步骤。为此，本文进行了两种不同的词嵌入模型，即GloVe和Word2Vec，用于向量化。在本研究中，采用了双向门控循环单元，因为存在积极和消极两种极性。然后，通过增强人类进化优化（EHEO）算法对其进行优化，从而改进超参数。研究结果表明，使用GloVe时，Bi-GRU/EHEO模型的精确率达到97.26%，召回率达到96.37%，准确率达到97.42%，F1分数达到96.30%。使用Word2Vec时，所提出的模型精确率达到98.54%，召回率达到97.75%，准确率达到97.54%，F1分数达到97.63%。将这些模型与其他模型进行了比较，如GRU，其在GloVe上的精确率、召回率、准确率和F1分数分别为89.24、90.14、89.57和89.68，在Word2Vec上的相应值分别为89.67、90.18、90.75和89.41；以及Bi-GRU，其在GloVe上的值分别为90.13、90.47、90.71和90.30，在Word2Vec上的值分别为90.31、90.76、90.67和90.53。所提出的情感分析方法在实际应用中显示出很大的潜力，如客户反馈评估、政治观点分析和社交媒体情感分析。通过利用这些模型的高效率和准确性，这些方法可以为不同行业提供一些实际解决方案，以预测趋势、改进决策过程和检查文本数据。
