Word2Vec approach for sentiment classification relating to hotel reviews

Abstract

In general, the existing works in sentiment classification concentrate only the syntactic context of words. It always disregards the sentiment of text. This work addresses this issue by applying Word2Vec to learn sentiment specific words embedded in texts, and then the similar words will be grouped as a same concept (or class) with sentiment information. Simply speaking, the aim of this work is to introduce a new task similar to word expansion or word similarity task, where this approach helps to discover words sharing the same semantics automatically, and then it is able to separate positive or negative sentiment in the end. The proposed method is validated through sentiment classification based on the employing of Support Vector Machine (SVM) algorithm. This approach may enable a more efficient solution for sentiment analysis because it can help to reduce the inherent ambiguity in natural language.