This paper describes a supervised approach we have designed for the topic-based message polarity classification. Given a message and a topic, we aim at (i) classifying the message on a two point scale, that is positive or negative sentiment toward that topic and (ii) classifying the message on a five-point scale, that is the message conveyed by that tweet toward the topic on a more fine-grained range. These two tasks have been proposed as subtasks of SemEval-2017 task 4. We have targeted them with the employment of IBM Watson that we leveraged to extract concepts and categories to enrich the vectorial space we have modeled to train our classifiers. We have used different classifiers for the two tasks on the provided training set and obtained good accuracy and F1-score values comparable to the SemEval 2017 competitors of those tasks.

Supervised Topic-Based Message Polarity Classification using Cognitive Computing

Federico Ibba;Diego Reforgiato Recupero
2018

Abstract

This paper describes a supervised approach we have designed for the topic-based message polarity classification. Given a message and a topic, we aim at (i) classifying the message on a two point scale, that is positive or negative sentiment toward that topic and (ii) classifying the message on a five-point scale, that is the message conveyed by that tweet toward the topic on a more fine-grained range. These two tasks have been proposed as subtasks of SemEval-2017 task 4. We have targeted them with the employment of IBM Watson that we leveraged to extract concepts and categories to enrich the vectorial space we have modeled to train our classifiers. We have used different classifiers for the two tasks on the provided training set and obtained good accuracy and F1-score values comparable to the SemEval 2017 competitors of those tasks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/254289
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