BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs

Autor:	Cliche, Mathieu
Rok vydání:	2017
Předmět:	Computer Science - Computation and Language Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams. Comment: Published in Proceedings of SemEval-2017, 8 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1704.06125 Zobrazit plný text záznamu View this record from Arxiv