基于模糊集的数字印度方案意见挖掘

P. Manoharan, L. Agilandeeswari, M. S. Praneeth
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引用次数: 0

摘要

本文描述了利用模糊集对数字印度(OMDI)方案进行意见挖掘的进展。根据人们的意见和评论,情绪分类器将对评论的情绪和极性水平进行分类。在这个现代世界,大多数人会提供他们的反馈或意见的产品已经增加。意见挖掘结果将有助于用户做出更好的决策。对于情感分类,使用朴素贝叶斯和模糊逻辑(直觉模糊集)。通过使用这些算法,我们定义了意见的极性等级,如积极,消极和中性。一般来说,情感分类将通过利用自然语言处理、机器学习、统计方法和分类方法来完成。通过挖掘模糊逻辑强大的推理潜力,我们根据人们的使用情况,为人们的评论赋予了极性。模糊逻辑根据意见词在实质中的用法赋予其连续的隶属度值来处理模糊性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Opinion mining for digital India scheme using fuzzy sets
In this paper, we describe the development of opinion mining for digital India (OMDI) scheme using fuzzy sets. According to people's opinions and reviews, the sentiment classifier will classify the emotion and polarity levels of the review. In this modern world, majority of the people will provide their feedback or opinions on the product that has been increased. The opinion mining results will be useful for the users to make better decision. For the classification of sentiment Naive Bayes and fuzzy logic (intuitionistic fuzzy sets) is utilised. By using these algorithms, we defined the polarity levels of the opinions such as positive, negative and neutral. In general, the sentiment classification will be done by utilising NLP, machine learning, statistical approach and classification methods. By mining powerful reasoning potential of fuzzy logics, we have accredited the polarities to the people's reviews according to their usage. Fuzzy logic deals with the vagueness by accrediting the continuous membership values to opinion words according to their usage in substance.
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