Abstract
A variety of independent research activities have recently been undertaken to explore the feasibility of incorporating social networking principles into the Internet of Things solutions. The resulting model, called the Social Internet of Things, has the potential to be more powerful and competitive in supporting new IoT applications and networking services. This paper’s main contribution is in sentiment analysis, which aims to predict aspect sentiments to improve the making of automated decisions and communication between associates in the social internet of things. In recent years, to analyze sentiment polarity at a subtle level, sentiment classification has become a primetime attraction. Current approaches commonly use the Long-Short Term Memory network to figure aspects and contexts separately. Usually, they perform sentiment classification using simple attention mechanisms and avoiding the bilateral information between sentences and their corresponding aspects. Therefore, the results are not satisfactory. This manuscript intends to develop a new Bidirectional gated recurrent unit model by depending on natural language processing for fully-featured mining to perform the aspect-level sentiment classification task. Our proposed model uses the Bidirectional gated recurrent unit network to acquire the dependency-based semantic analysis of sentences and their corresponding terms compared to earlier work. At the same time, it proposes a method to learn the sentiment polarity of terms in sentences. To check out our model’s achievements, we perform several experiments on datasets, namely, (LAPTOP, RESTUARANT, and TWITTER). Our experiment results demonstrate that our model has achieved compelling performance and efficiency improvements in aspect sentiment classification compared with several existing models.
Highlights
In the past years, the world has seen the number of IoT devices increasing day by day with the internet's explosive growth
IoT is a new technology that has changed the old way of living to a high-tech lifestyle
The authors[10] proposed a framework that utilizes social networks data for aspect-based sentiment analysis, enabling IoT devices to better respond to user-related services
Summary
The world has seen the number of IoT devices increasing day by day with the internet's explosive growth. SIoT depends on the topological structure of social networks and their entities, defined by intelligent hardware and users, to create efficient models that can capture social networks' characteristics using social relationships Such characteristics include useful information about human activities and actions, which SIoT networks may use in combination with perceptual monitoring technologies to make intelligent decisions about network implementation and service enhancements. Social networking sites and applications generally produce a considerable amount of data, a precious resource that can help people or machines make decisions by analyzing inherent opinion or sentiment information These activities are essential because they have the ability to bring substantial benefits to the lives of individuals and society.
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