The global market for bone-conduction headphones is expected to continue to grow, as the COVID-19 pandemic has increased opportunities for telecommunicating, or working from home, as opposed to going to the office every day. Increased teleworking opportunities have led to more online conferencing, and as a result, the market for bone-conduction headphones is growing even more rapidly. Early adopters are very important in today's marketing. They are very sensitive to public trends and tend to adopt new products quickly. Many early adopters gather information on their own, make their own decisions based on the information they gather, and purchase products. And they tend to spread the word about how good the product is and what makes it different from others from a consumer perspective. During a period of market expansion, the diffusion of the word-of-mouth they transmit is key to the diffusion of the product. In this study, we use online word-of-mouth data to study early adopeters’ knowledge of new products.

The pandemic caused by COVID-19 has also affected the camera industry, and various events have been cancelled. In addition, the recent improvement in the performance of cameras installed in smartphones has reduced the demand for replacement cameras, as it is easy to take pictures without carrying a camera. For customers, online word-of-mouth is what they refer to when purchasing a product. This data is important not only for customers, but also for companies. In this research, we will use online word-of-mouth data and focus not only on numerical data but also on textual data and use text mining to learn knowledge about the decision to replace a camera.

We introduce a geometry of interaction model for Mazza's multiport interaction combinators, a graph-theoretic formalism which is able to faithfully capture concurrent computation as embodied by process algebras like the π-calculus. The introduced model is based on token machines in which not one but multiple tokens are allowed to traverse the underlying net at the same time. We prove soundness and adequacy of the introduced model. The former is proved as a simulation result between the token machines one obtains along any reduction sequence. The latter is obtained by a fine analysis of convergence, both in nets and in token machines.