Semantic Differentials and Word2Vec-Google-News-300 for SCM-Embedding Model

Our investigation is grounded in the theoretical framework of semantic differentials theory as proposed by Osgood in 1958. To build our SCM-embedding model, we utilized the widely-used 'word2vec-google-new-300' word embedding model, which was released by Google. This original word embedding model features pre-trained vectors that were specifically trained on a subset of the Google News dataset, encompassing approximately 100 billion words. The model consists of 300-dimensional vectors for over 3 million words and phrases, and a comprehensive breakdown of its specifications is provided in the following sections.