Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data

This application, presents a comparison of different deep learning methods used for sentiment analysis in Twitter dataset. In deep learning (DL) techniques, which contribute at the same time to the solution of a wide range of problems, gained popularity among researchers. In these two categories of neural networks are utilized, convolutional neural networks (CNN), which are especially performant in the area of image processing and recurrent neural networks (RNN) which are applied with success in natural language processing (NLP) tasks. 

In this work we evaluate and compare ensembles and combinations of CNN and a category of RNN the long shorterm-memory (LSTM) networks. Additionally, we compare different word embedding systems such as the Word2Vec and the global vectors for word representation (GloVe) models. For the evaluation of those methods, we used data provided by the international workshop on semantic evaluation (SemEval), which is one of the most popular international workshops on the area. Various tests and combinations are applied and best scoring values for each model are compared in terms of their performance. This study contributes to the field of sentiment analysis by analyzing the performances, advantages and limitations of the above methods with an evaluation procedure under a single testing framework with the same dataset and computing environment.

Keywords: Sentiment Analysis, Deep Learning, Convolutional Neural Networks, LSTM, Word Embedding Models, Twitter Data.