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Text-based sentiment analysis plays a very important role in understanding customer opinions and preferences. But despite extensive research in sentiment and emotion analysis in text, a notable gap exists in understanding code-mixed texts. To address this, we propose an end-to-end transformer based model.
Performed feature selection using F-score method to filter out the important features in order to optimize the performance of Linear SVM machine learning model. The accuracy achieved was above 63%.
Given an instance of set of nodes in a social network graph, the aim is to find the influencing important users and to predict the likelihood of a future association between two nodes, knowing that there is no association between the nodes in the current state of the graph.
A library that helps you evaluate the performance of annotator systems, for example. Calculates evaluation metrics like precision, recall and F1-score.