Sentence-Level Sentiment Analysis Using Feature Vectors from Word Embeddings
Toshitaka Hayashi, Hamido Fujita · Frontiers in artificial intelligence and applications · 2018
Word2vec have been proven to facilitate various NLP tasks. We suppose that the vector space of word2vec can be divided into positive and negative. Hence, word2vec is applicable to Sentiment Analysis tasks. In this paper, we proposed supervised approach for Sentence-level Sentiment Analysis. We utilize pre trained Word Embeddings to extract features from Sentence. We train feature vectors and their polarities to make classification model. After training, we use the model for predicting new sentence's polarity. We compare our method against state of the arts and discuss about how to improve our method.