Abstract
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 Naive 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.