Abstract
Sentiment polarity classification in social media is a very important task, as it enables gathering trends on particular subjects given a set of opinions. Currently, a great advance has been made by using deep learning techniques, such as word embeddings, recurrent neural networks, and encoders, such as BERT. Unfortunately, these techniques require large amounts of data, which, in some cases, is not available. In order to model this situation, challenges, such as the Spanish TASS organized by the Spanish Society for Natural Language Processing (SEPLN), have been proposed, which pose particular difficulties: First, an unwieldy balance in the training and the test set, being this latter more than eight times the size of the training set. Another difficulty is the marked unbalance in the distribution of classes, which is also different between both sets. Finally, there are four different labels, which create the need to adapt current classifications methods for multiclass handling. Traditional machine learning methods, such as Naïve Bayes, Logistic Regression, and Support Vector Machines, achieve modest performance in these conditions, but used as an ensemble it is possible to attain competitive execution. Several strategies to build classifier ensembles have been proposed; this paper proposes estimating an optimal weighting scheme using a Differential Evolution algorithm focused on dealing with particular issues that multiclass classification and unbalanced corpora pose. The ensemble with the proposed optimized weighting scheme is able to improve the classification results on the full test set of the TASS challenge (General corpus), achieving state of the art performance when compared with other works on this task, which make no use of NLP techniques.
Highlights
Sentiment polarity refers to the opinion people have about an entity.Several machine learning methods have been used to automatically determine polarity of text published on Internet [1,2,3,4]
Several strategies to build classifier ensembles have been proposed; this paper proposes estimating an optimal weighting scheme using a Differential Evolution algorithm focused on dealing with particular issues that multiclass classification and unbalanced corpora pose
The ensemble with the proposed optimized weighting scheme is able to improve the classification results on the full test set of the TASS challenge (General corpus), achieving state of the art performance when compared with other works on this task, which make no use of NLP techniques
Summary
Sentiment polarity refers to the opinion people have about an entity (e.g., film, service, news, etc.).Several machine learning methods have been used to automatically determine polarity of text published on Internet [1,2,3,4]. Polarity is automatically determined in various domains using different approaches, for example, in health prediction [5,6,7,8] or transportation [9]. Positive or negative); classifiers are trained on tagged examples and they generate a model that relates features to the corresponding. Ensemble learning uses a set of classifiers to combine their predictions in different ways. [10] showed that an ensemble of classifiers is more accurate than its individual members if each of these members has an error rate less than 0.5, and they generate different errors when classifying new instances—i.e., members are accurate and diverse. There are two determining factors concerning a voting ensemble that have been studied, the set of classifiers to be combined [11] and the weight assigned to each classifier [12].
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