Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter
This work addresses sentiment analysis for social media applications, but it is incremental as it builds on existing methods for a competition task.
The paper tackled sentiment detection on Twitter by adapting the Amobee system, which combined RNN models trained on a Twitter sentiment treebank with other classifiers, achieving 3rd place in the 5-label classification sub-task at SemEval 2017.
This paper describes the Amobee sentiment analysis system, adapted to compete in SemEval 2017 task 4. The system consists of two parts: a supervised training of RNN models based on a Twitter sentiment treebank, and the use of feedforward NN, Naive Bayes and logistic regression classifiers to produce predictions for the different sub-tasks. The algorithm reached the 3rd place on the 5-label classification task (sub-task C).