1 research outputs found
TF-IDFC-RF: A Novel Supervised Term Weighting Scheme
Sentiment Analysis is a branch of Affective Computing usually considered a
binary classification task. In this line of reasoning, Sentiment Analysis can
be applied in several contexts to classify the attitude expressed in text
samples, for example, movie reviews, sarcasm, among others. A common approach
to represent text samples is the use of the Vector Space Model to compute
numerical feature vectors consisting of the weight of terms. The most popular
term weighting scheme is TF-IDF (Term Frequency - Inverse Document Frequency).
It is an Unsupervised Weighting Scheme (UWS) since it does not consider the
class information in the weighting of terms. Apart from that, there are
Supervised Weighting Schemes (SWS), which consider the class information on
term weighting calculation. Several SWS have been recently proposed,
demonstrating better results than TF-IDF. In this scenario, this work presents
a comparative study on different term weighting schemes and proposes a novel
supervised term weighting scheme, named as TF-IDFC-RF (Term Frequency - Inverse
Document Frequency in Class - Relevance Frequency). The effectiveness of
TF-IDFC-RF is validated with SVM (Support Vector Machine) and NB (Naive Bayes)
classifiers on four commonly used Sentiment Analysis datasets. TF-IDFC-RF shows
promising results, outperforming all other weighting schemes on two datasets