This article is an extension of a previous one I wrote when I was experimenting sentiment analysis on twitter data. Back in the time, I explored a simple model: a two-layer feed-forward neural network trained on keras. The input tweets were represented as document vectors resulting from a weighted average of the embeddings of the words composing the tweet. The embedding I used was a word2vec model I trained from scratch on the corpus using gensim. The task was a binary classification and I was able with this setting to achieve 79% accuracy. The goal of this post is to explore other NLP models trained on the same dataset and then benchmark their respective performance on a given test set. We'll go through different models: from simple ones relying on a bag-of-word representation to a heavy machinery deploying convolutional/recurrent networks: We'll see if we'll score more than 79% accuracy! Let's investigate !