Sentiment Analysis-Based Categorized Opinions Expressed in Feedback Forums Using Deep Learning Technique and Message Queue Architecture

U. Kumar
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Abstract

Sentiment analysis is a sub-field of natural language processing (NLP). In sentiment analysis the sentiment behind the piece of data is tried to know, this data can be a review of a product by a customer or a comment on some social media platform. Analysing large amounts of data is still an easy task for small retail websites and business owners. Deep learning (DL) has made a great revolution in the field of speech and image recognition. Mature deep learning neural network i.e. convolution neural network (CNN) has completely changed the field of NLP. This paper proposed a high accuracy, efficient, scalable, reliable and secure solution to cater all the needs of business owners and institutes for sentiment analysis with DL model, a browser based GUI interface for easy accessibility to all the non-technical folks and a dashboard having graphical representations of their results. The proposed sentiment analysis based model has achieved 93.55% accuracy which has outperformed other models.
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基于情感分析的基于深度学习技术和消息队列架构的反馈论坛分类意见表达
情感分析是自然语言处理(NLP)的一个分支。在情感分析中,试图了解数据背后的情感,这些数据可以是客户对产品的评论,也可以是某些社交媒体平台上的评论。对于小型零售网站和企业主来说,分析大量数据仍然是一项简单的任务。深度学习(DL)在语音和图像识别领域掀起了一场巨大的革命。成熟的深度学习神经网络即卷积神经网络(CNN)已经彻底改变了自然语言处理领域。本文提出了一个高精度、高效、可扩展、可靠和安全的解决方案,以满足企业主和机构对深度学习模型的情感分析的所有需求,一个基于浏览器的GUI界面,方便所有非技术人员访问,以及一个具有图形化表示结果的仪表板。基于情感分析的模型准确率达到93.55%,优于其他模型。
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