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Development of IoT mining machine for Twitter sentiment analysis: Mining in the cloud and results on the mirror

机译：开发用于Twitter情绪分析的IoT挖矿机：在云中挖矿并在镜像上获得结果

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Microblogs sentiment analysis of people's attitudes, appraisals and emotions has become one of the most active research areas for business marketing, decision making, political campaigns, and alike. As people publish short snippets of texts through the social networks expressing their ideas, thoughts and opinions, an instant and reliable mining machine should be utilized. In this paper, we proposed an IoT mining machine for Twitter sentiment analysis. Firstly, we used Twitter's API for harvesting tweets in real time. Then, a mining engine was developed on the Raspberry Pi single-board microcomputer as an IoT platform due to its availability and connectivity. The IoT device was programmed for sentiment analysis and opinion mining using state-of-the-art Naïve Bayes classifier which after training was used to classify the trending tweets into either positive or negative. We used a gold standard dataset from SemEval 2017 for training our classifier which achieved 0.992 of accuracy. We aggregated the sentiments of tweets streamed in daily trend hashtags into visualized graphs. Finally, the visualized results from opinion mining were displayed on two-way smart mirror without any need for application installment. Our experimental results on the IoT mining machine demonstrate its feasibility and effectiveness.
机译：人们对态度，评估和情感的微博情感分析已成为企业营销，决策，政治运动等方面最活跃的研究领域之一。当人们通过社交网络发布简短的文本片段以表达他们的想法，思想和观点时，应使用即时可靠的采矿机。在本文中，我们提出了一种用于Twitter情绪分析的IoT挖掘机器。首先，我们使用Twitter的API实时收集推文。然后，由于其可用性和连接性，在Raspberry Pi单板微计算机上开发了一个挖掘引擎作为IoT平台。使用最先进的朴素贝叶斯分类器对物联网设备进行了情感分析和观点挖掘编程，该分类器在经过训练后用于将趋势推文分类为正面或负面。我们使用了SemEval 2017的黄金标准数据集来训练我们的分类器，该分类器的准确度达到了0.992。我们将日常趋势标签中流传的tweet的情感汇总到可视化的图表中。最后，意见挖掘的可视化结果显示在双向智能镜上，无需安装任何应用程序。我们在IoT采矿机上的实验结果证明了其可行性和有效性。

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