The task of sentiment analysis tries to predict the affective state of a document by examining its content and metadata through the application of machine learning techniques. Recent advances in the field consider sentiment to be a multi-dimensional quantity that pertains to different interpretations (or aspects), rather than a single one. Based on earlier research, the current work examines the said task in the framework of a larger architecture that crawls documents from various online sources. Subsequently, the collected data are pre-processed, in order to extract useful features that assist the machine learning algorithms in the sentiment analysis task. More specifically, the words that comprise each text are mapped to a neural embedding space and are provided to a hybrid, bi-directional long short-term memory network, coupled with convolutional layers and an attention mechanism that outputs the final textual features. Additionally, a number of document metadata are extracted, including the number of a document’s repetitions in the collected corpus (i.e. number of reposts/retweets), the frequency and type of emoji ideograms and the presence of keywords, either extracted automatically or assigned manually, in the form of hashtags. The novelty of the proposed approach lies in the semantic annotation of the retrieved keywords, since an ontology-based knowledge management system is queried, with the purpose of retrieving the classes the aforementioned keywords belong to. Finally, all features are provided to a fully connected, multi-layered, feed-forward artificial neural network that performs the analysis task. The overall architecture is compared, on a manually collected corpus of documents, with two other state-of-the-art approaches, achieving optimal results in identifying negative sentiment, which is of particular interest to certain parties (like for example, companies) that are interested in measuring their online reputation.

中文翻译：

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基于知识的深度学习架构，用于基于方面的情感分析

情感分析的任务试图通过应用机器学习技术检查文档的内容和元数据来预测文档的情感状态。该领域的最新进展将情绪视为与不同解释（或方面）相关的多维量，而不是单个量。基于早期的研究，目前的工作在一个更大的架构框架中检查上述任务，该架构从各种在线资源中抓取文档。随后，对收集到的数据进行预处理，以提取有用的特征，帮助机器学习算法完成情感分析任务。更具体地说，包含每个文本的单词被映射到一个神经嵌入空间，并提供给一个混合的双向长短期记忆网络，再加上卷积层和输出最终文本特征的注意力机制。此外，还提取了许多文档元数据，包括在收集的语料库中文档的重复次数（即转发/转发的次数）、表情符号表意文字的频率和类型以及关键字的存在，无论是自动提取还是手动分配，以标签的形式。该方法的新颖之处在于检索关键字的语义注释，因为查询基于本体的知识管理系统，目的是检索上述关键字所属的类。最后，所有特征都提供给执行分析任务的全连接、多层、前馈人工神经网络。整体架构对比，