Twitter Sentiment Analysis Experiments Using Word Embeddings on Datasets of Various Scales

Sentiment analysis is a popular research topic in social media analysis and natural language processing. In this paper, we present the details and evaluation results of our Twitter sentiment analysis experiments which are based on word embeddings vectors such as word2vec and doc2vec, using an ANN classifier. In these experiments, we utilized two publicly available sentiment analysis datasets and four smaller datasets derived from these datasets, in addition to a publicly available trained vector model over 400 million tweets. The evaluation results are accompanied with discussions and future research directions based on the current study. One of the main conclusions drawn from the experiments is that filtering out the emoticons in the tweets could be a facilitating factor for sentiment analysis on tweets.