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使用长期短期记忆和修订的矮人蒙古斯优化算法进行情绪分析

Haisheng Deng¹, Ahmed Alkhayyat²

¹Xijing University, Xi'an, 710123, Shaanxi, China.

Scientific reports

|May 17, 2025

概括

本研究介绍了一种优化的长期短期记忆 (LSTM) 模型,使用修改后的矮人蒙古斯优化 (ADMO) 算法进行情绪分析. 拟议的LSTM-ADMO模型实现了高精度,在基准数据集上表现优于其他方法.

科学领域:

自然语言处理自然语言处理.
机器学习机器学习
人工智能的人工智能

背景情况:

情感分析至关重要,但由于文本复杂性,如缩写和错误,具有挑战性.
传统方法在不同的文本数据中难以检测微妙的情绪.

研究的目的:

通过使用先进的机器学习技术来提高情绪分析性能.
为了研究不同词嵌入模型与优化的LSTM架构相结合的有效性.

主要方法:

使用GloVe和Word2Vec进行文本向量化.
采用了优化的长短期记忆 (LSTM) 模型,并使用了修改后的矮人蒙古斯优化 (ADMO) 算法进行超参数调整.
在IMDB和SST-2数据集上评估模型.

主要成果:

LSTM-ADMO模型实现了高精度,在SST-2上达到97.84%的Word2Vec.
Word2Vec和GloVe之间的性能差异很小.
与现有方法相比,拟议的模型表现出优越的性能.

结论:

优化的LSTM-ADMO模型对于情绪分析非常有效,即使是复杂的文本数据.
Word2Vec和GloVe都是适合这种方法的词嵌入技术.
这项研究强调了对NLP深度学习模型的元启发性优化潜力.

关键词:

修改的矮人松鼠优化 (ADMO) 算法世界杯的世界杯.长时间的短期记忆 (LSTM)情绪分析是一种情绪分析.在Word2Vec中使用.

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