An Efficient Combined Approach for Sentiment Analysis using SVM and HARN Algorithms

2018 International Conference on Communication, Computing and Internet of Things (IC3IoT) Pub Date : 2018-02-01 DOI:10.1109/IC3IOT.2018.8668196

V. N. D. Duvvuri, Venkata Rajini Kanth Thatiparti, Mounika Kakollu, Sowjanya Swathi Nambhatla, Ravi Vemagiri, Girish Varma Vegesna

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Abstract

In olden days only MNC companies used to formulate the data and make use of it. But nowadays each and every individual is creating a bulk amount of data and using such a huge data. For example, we have numerous products available in one of the reputed websites viz. Amazon website in which most of the people are buying a vast amount of products and readily provide their esteemed reviews on that particular product. Data is generated as explained above. Google will produce more than 20PB of data, while Facebook generates more than 5PB of messages and so on. Analyzing that huge amount of data is troublesome to humans. To solve this challenging task, sentiment analysis comes into consideration. In our analysis, we have developed an innovative way for finding sentiment analysis at document level by using SVM and HARN’s algorithm. It is proved to be one of the best ways to analyze customer’s opinion on a product at the document level.

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一种基于支持向量机和HARN算法的情感分析方法

在过去，只有跨国公司才会编制数据并加以利用。但现在，每个人都在创造大量的数据，并使用如此庞大的数据。例如，我们有许多产品可以在一个著名的网站，即亚马逊网站，其中大多数人都在购买大量的产品，并随时提供他们对该特定产品的尊敬的评论。生成的数据如上所述。谷歌将产生超过20PB的数据，而Facebook将产生超过5PB的消息，以此类推。分析如此庞大的数据对人类来说是很麻烦的。为了解决这一具有挑战性的任务，情感分析被考虑在内。在我们的分析中，我们开发了一种创新的方法，通过使用支持向量机和HARN的算法在文档级别找到情感分析。它被证明是在文档层面分析客户对产品意见的最佳方法之一。

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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2018 International Conference on Communication, Computing and Internet of Things (IC3IoT)

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