Open AccessAn Analysis of the Sentiment Classification of Short Messages Using Word2Vec
Author(s) -
Raul de Araújo Lima,
Paulo T. Guerra
Publication year - 2018
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2018.4436
Subject(s) - word2vec , computer science , sentiment analysis , polarity (international relations) , word (group theory) , process (computing) , artificial intelligence , natural language processing , information retrieval , data mining , mathematics , geometry , embedding , genetics , biology , cell , operating system
Sentiment analisys and the polarity classification of texts constitute one of the main tools currently used by companies and organizations for the most varied purposes. This work presents an analysis of the use of word embeddings, built through Word2Vec, in the process of features extraction for polarity classification of short messages written in English. The texts used were extracted from Twitter and the results obtained show that, in spite of the possible need to use larger textual bases to obtain better vectors, Word2Vec is a promising tool for the features extraction of textual data, contributing to obtain good classification results.