A survey paper on the latest techniques for implicit feature extraction using CCC method

Ameya Parkar, Rajni Bhalla
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Abstract

Sentiment Analysis is gathering a lot of attention nowadays as a lot of online data is gathered through blogs, ecommerce websites, product reviews, etc. which people are expressing online. This data is extracted by companies to judge if their products are having a positive outlook or a negative outlook. However, when people express their opinions, they mention not only about the entity but also about the aspects of the entity. A lot of research has gone ahead on gathering opinions on aspects, especially explicit aspects. But little work is done on gathering implicit aspects. This paper provides a survey on different techniques used by researchers to gather implicit aspects. At the end, we propose a methodology to extract implicit aspects from reviews. We propose co-occurrence matrix for all opinions and aspects followed by clustering technique to gather all aspects which are similar in one cluster followed by classification using machine learning techniques. The proposed framework will give suggestions to different researchers in the domain on extracting implicit aspects.
综述了基于CCC方法的隐式特征提取的最新技术
如今,随着人们通过博客、电子商务网站、产品评论等方式收集大量在线数据,情感分析正受到越来越多的关注。这些数据由公司提取,以判断他们的产品前景是积极的还是消极的。然而,当人们表达他们的意见时,他们不仅提到实体,还提到实体的各个方面。在收集各方面的意见,特别是显性方面的意见方面,已经进行了大量的研究。但是在收集隐含方面做的工作很少。本文综述了研究者收集隐性方面的不同方法。最后,我们提出了一种从评论中提取隐含方面的方法。我们提出了所有观点和方面的共现矩阵,然后采用聚类技术将所有相似的方面聚集在一个聚类中,然后使用机器学习技术进行分类。该框架将为不同领域的研究者在隐式方面的提取提供建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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