Twitter is a social networking site that enables users to communicate with their followers by sending them short messages known as "tweets." Each tweet has a character limit of 280 characters. The minimum limit of tweets resulted in writing short tweets and increased use of word variations. This makes tweets difficult to understand without the help of the topic, thus tweets should be classified. This study aims to classify topics of Twitter using word2vec feature expansion to decrease vocabulary ambiguities in topic classification. This type of research is system design research. Feature expansion is a machine learning technique used to extract new features (or variables) from the dataset's existing features. A model's complexity and expressive power are intended to be increased through feature expansion in order to improve performance and generalization. Data were processed using Convolutional Neural Network (CNN). The results indicate that there is an important contribution in increasing understanding of topic classification in Twitter data with Word2Vec, and the CNN application is able to assist some obstacles in analyzing short text with high word variations.