Due to the remarkable increase in e-commerce transactions, people try to have an appropriate choice of purchase through considering other people's reflected experience in product's or service's reviews. Automatic analysis of such corpus requires enhanced developed algorithms based on natural language processing and opinion mining. Moreover, the linguistic differences make extending existing algorithms from one language to another challenging and in some cases impossible. Opinion mining focuses on different subjects of review analysis such as spam detection, aspect elicitation and polarity allocation. In this article, we focus on detection of explicit aspect and propose a methodology to overcome some difficult and problematic aspect compounds in the form of multi- words format in Persian language. Our approach proposes the construction of a directed weighted graph (ADG structure) based on some yielded information from FP-Growth frequent pattern identification algorithm on our corpus of Persian sentence. Traversing some special paths within the ADG graph according to our developed rules could lead us to the extraction of problematic multi-word aspects. We utilize Neo4j NoSQL graph database environment and its Cypher query language in order to create the ADG graph and access the desired paths that reflects our developed rules on the ADG structure which lead us to extract the multi-word aspects. The evaluation of our methodology with the existing approaches on the issue of aspect derivation in Persian language including ELDA, SAM, an MMI-based and an LRT-based algorithms indicates the robustness of our approach.

中文翻译：

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采用 28 nm CMOS 技术合成用于汽车雷达应用的毫米波宽带接收器


由于电子商务交易的显着增加，人们试图通过考虑其他人在产品或服务评论中反映的体验来做出适当的购买选择。此类语料库的自动分析需要基于自然语言处理和意见挖掘的增强型开发算法。此外，语言差异使得将现有算法从一种语言扩展到另一种语言具有挑战性，在某些情况下甚至是不可能的。意见挖掘侧重于评论分析的不同主题，例如垃圾邮件检测、方面启发和极性分配。在本文中，我们重点关注显式方面的检测，并提出了一种方法来克服波斯语中多词格式形式的一些困难和有问题的方面复合。我们的方法提出了基于 FP-Growth 频繁模式识别算法在我们的波斯语句子语料库上产生的一些信息构建有向加权图（ADG 结构）。根据我们制定的规则遍历 ADG 图中的一些特殊路径可以引导我们提取有问题的多词方面。我们利用 Neo4j NoSQL 图形数据库环境及其 Cypher 查询语言来创建 ADG 图形并访问反映我们在 ADG 结构上开发的规则的所需路径，从而引导我们提取多词方面。对我们的方法与波斯语方面推导问题上的现有方法（包括 ELDA、SAM、基于 MMI 和基于 LRT 的算法）的评估表明了我们方法的稳健性。