Developing a highly efficient transformer from an embedding public opinion network into a low-dimensional vector space contributes a lot to many research areas such as vertex classification, community detection and public opinion analysis, etc. Most existing network embedding methods have chosen to analysis in social networks. However, constructing a social network from public opinions is very sparsely, which would serve as an effective way to capture and process public opinions. On top of that, social network can only reflect the social relationships between nodes while the information derived from opinions is neglected. Hence, a network that incorporates opinion features of nodes into social networks is reported. This study evaluates the similarity of opinions from different nodes and connects them with enough similarity. The final public opinion network would certainly be denser than the social network. Experimental results show that researchers might give top priority to use the approach of public opinion network embedding compared with the regular social network methods, especially when the sentiment orientation of opinions is explicit.
