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MS-SAE: A General Model of Sentiment Analysis Based on Multimode Semantic Extraction and Sentiment Attention Enhancement Mechanism

机译：MS-SAE：基于多模式语义提取和情感注意增强机制的情感分析通用模型

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Recently, there is a lot of research on sentiment analysis. When existing models extract text features, semantic information can't be fully obtained, because they ignore context connection between historical texts and current texts. Meanwhile, models cannot self-optimize extraction algorithms, making key sentiment semantics be neglected, because they can't track and feedback analysis results. Furthermore, models extract insufficient sentiment features, resulting in imperfect semantic extraction and unsatisfactory analysis results, because they only extract POS features. To solve the above problems, we propose a general model of sentiment analysis based on multimode semantic extraction and sentiment attention enhancement mechanism (MS-SAE). The model includes a multimode semantic extraction processing (a multi-mode semantic extraction module and a sentiment attention enhancement mechanism), an extended dictionary (ExWordNet) and a sentiment analysis module. The multimode semantic extraction module extracts semantic features from multiple perspectives and pays close attention to extracted features, which solves the problem of insufficient semantic extraction. We propose a sentiment attention enhancement mechanism to solve the problem that sentiment semantics is neglected. We construct a general extended dictionary to support MS-SAE in semantic extraction processing. The LSTM-based sentiment analysis module ensures the accuracy of sentiment analysis. We evaluate MS-SAE on SST-2, MR and Subj datasets. Extensive experiments have been conducted and the results demonstrate that MS-SAE could achieve better sentiment analysis performance than the state-of-the-art algorithms in accuracy. It solves the problems including poor understanding of text semantics and errors in analysis results.
机译：最近，关于情绪分析的研究很多。现有模型提取文本特征时，无法完全获得语义信息，因为它们会忽略历史文本和当前文本之间的上下文连接。同时，模型无法自优化提取算法，从而使关键情感语义被忽略，因为它们无法跟踪和反馈分析结果。此外，由于模型仅提取POS特征，因此提取的情绪特征不足，导致语义提取不完善，分析结果不理想。为解决上述问题，我们提出了一种基于多模式语义提取和情感注意增强机制（MS-SAE）的情感分析通用模型。该模型包括多模式语义提取处理（多模式语义提取模块和情感注意增强机制），扩展词典（ExWordNet）和情感分析模块。多模式语义提取模块从多个角度提取语义特征，并密切关注提取的特征，解决了语义提取不足的问题。为了解决情感语义被忽视的问题，我们提出了一种情感注意力增强机制。我们构造了一个通用的扩展字典，以在语义提取处理中支持MS-SAE。基于LSTM的情绪分析模块可确保情绪分析的准确性。我们评估SST-2，MR和Subj数据集上的MS-SAE。进行了广泛的实验，结果表明，MS-SAE的准确度比最先进的算法可实现更好的情感分析性能。解决了对文本语义理解不充分，分析结果错误等问题。

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