Sentimental Analysis on Social Media Comments with Recurring Models and Pretrained Word Embeddings in Portuguese
Cristian Muoz Villalobos, Leonardo A. F. Mendoza, Harold Dias de Mello, César Hernando Valencia Niño, Alvaro Orjuela, Ricardo Tanscheit, Marco Aurélio C. Pacheco · 2022
Natural Language Processing (NLP) techniques are increasingly powerful for interpreting a person’s feelings and reaction to a product or service. Sentiment analysis has become a fundamental tool for this interpretation, and it has studies in languages other than English. This type of application is uncommon and unheard of in Portuguese. This article presents a sentiment analysis classification based on Portuguese social media comments. Representation of word embeddings with both pre-trained Glove and Word2Vec models were generated through a corpus entirely in Portuguese. This article presents a set of results with different models of pre-trained layers and deep learning models exclusive to the Portuguese language on social networks. Two classification models were used and compared: (i) Bidirectional Long Short-Term Memory (BI-LSTM) and (ii) Bidirectional Gated Recurrent Unit (BI-GRU), achieving accuracy results of 99.1