Comparison of Different Modeling Techniques for Flemish Twitter Sentiment Analysis

Abstract

Microblogging websites such as Twitter have caused sentiment analysis research to increase in popularity over the last several decades. However, most studies focus on the English language, which leaves other languages underrepresented. Therefore, in this paper, we compare several modeling techniques for sentiment analysis using a new dataset containing Flemish tweets. The key contribution of our paper lies in its innovative experimental design: we compared different preprocessing techniques and vector representations to find the best-performing combination for a Flemish dataset. We compared models belonging to four different categories: lexicon-based methods, traditional machine-learning models, neural networks, and attention-based models. We found that more preprocessing leads to better results, but the best-performing vector representation approach depends on the model applied. Moreover, an immense gap was observed between the performances of the lexicon-based approaches and those of the other models. The traditional machine learning approaches and the neural networks produced similar results, but the attention-based model was the best-performing technique. Nevertheless, a tradeoff should be made between computational expenses and performance gains.

Evaluating text classification: A benchmark study

“…Both preprocessing methods reduce the words to a root word. Lemmatization, however, also makes sure that the resulting word is an existing word (Reusens et al, 2022). Therefore, to preserve existing words when utilizing pretrained embeddings, we applied lemmatization as was done in (Alaparthi and Mishra, 2021).…”

Improving sentiment analysis using text network features within different machine learning algorithms

“…This study employed machine learning models like NB, SVM, convolutional neural networks (CNN), LSTM, and bidirectional long short-term memory to extract sentiment and ratings from traveler reviews (BiLSTM). Deep learning models based on BiLSTM are more efficient and accurate than machine learning algorithms, according to the study's findings [17]. The purpose of the project [18] is to analyze and forecast customer reviews from the Yelp website, and the initial data set was filtered to solely include insurance ratings.…”