Abstract

In recent years, sentimental analysis has been broadly investigated to extract information to identify whether it is positive, negative or neutral. Sentimental analysis can be broadly performed in social media content, survey response and review. Still, it faces issues while detecting and analyzing social media content. Moreover, a social media network contains indirect sentiments and natural language ambiguities make it complicated to classify the words. Thus, the aspect-based sentiment analysis (ABSA) is emerged to develop explicating extraction methods by utilizing the syntactic parsers to make use of the relation among sentiments and aspects terms. Along with this extraction method, the word embedding is performed through Word2Vec methods to attain a low-dimensional vector depiction of text, which could not capture valuable information. Thus, it aims to design a novel ABSA model using the optimized neural network along with optimal text feature extraction. Initially, various data is collected through the benchmark dataset are given to the image pre-processing. Then, it might undergo different techniques like stemming, stop word removal as well as punctuation removal. Then, the preprocessed data are further given into the feature extraction phase to attain adequate extracted aspects. Then, it further undergoes for deep feature extraction stage, where the text conventional neural network and Glove embedding are utilized to obtain the deep features. Further, the feature concatenation is done to attain the optimization for polarity-based weighted features utilized by the enhanced hybrid optimization algorithm called hybrid Chameleon rat swarm optimization (HCRSO) for improving the performance in sentiment analysis. The optimal features are selected by the HCRSO that provides the polarity-based-weight features; thus, it separates the polarity, and the weighted features are occurred by multiplying the weight with polarities. Especially, the optimized features of polarity-based weighted features and also the parameters of epochs and hidden neuron count of neural attention mechanism-based long short-term network (NAM-LSTM) are optimized using the HCRSO algorithm. The weighted feature is applied by incorporating the NAM-LSTM and proposed HCRSO algorithm for improving the model efficiency. The empirical outcome of the recommended method shows 94% and 93% regarding accuracy and specificity. Thus, the experimental outcomes of the proposed ABSA model reveal the model’s efficiency while validating with other conventional approaches.

中文翻译：

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基于方面的情感分析框架的优化神经注意机制，具有基于最佳极性的加权特征

摘要

近年来，情感分析已被广泛研究，以提取信息来识别其是积极的、消极的还是中性的。情感分析可以广泛地在社交媒体内容、调查响应和评论中进行。尽管如此，它在检测和分析社交媒体内容时仍面临问题。此外，社交媒体网络包含间接情感，自然语言的歧义使得单词分类变得复杂。因此，基于方面的情感分析（ABSA）应运而生，通过利用句法解析器来利用情感和方面术语之间的关系来开发解释性提取方法。与这种提取方法一起，通过Word2Vec方法进行词嵌入以获得文本的低维向量描述，这无法捕获有价值的信息。因此，其目的是使用优化的神经网络和最佳文本特征提取来设计一种新颖的 ABSA 模型。最初，通过基准数据集收集的各种数据被提供给图像预处理。然后，它可能会经历不同的技术，例如词干提取、停用词删除以及标点符号删除。然后，将预处理后的数据进一步送入特征提取阶段，以获得足够的提取特征。然后，进一步进行深度特征提取阶段，利用文本常规神经网络和Glove嵌入来获取深层特征。此外，进行特征串联是为了实现基于极性的加权特征的优化，该算法由称为混合变色龙大鼠群优化（HCRSO）的增强型混合优化算法利用，以提高情感分析的性能。最优特征由HCRSO选择，提供基于极性的权重特征；因此，它分离了极性，并通过将权重与极性相乘来产生加权特征。特别是，使用HCRSO算法优化了基于极性的加权特征的优化特征以及基于神经注意机制的长短期网络（NAM-LSTM）的历元参数和隐藏神经元数量。通过结合 NAM-LSTM 和提出的 HCRSO 算法应用加权特征来提高模型效率。推荐方法的实证结果显示准确性和特异性分别为 94% 和 93%。因此，所提出的 ABSA 模型的实验结果揭示了该模型的效率，同时用其他传统方法进行了验证。