NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks through a Multi-Domain Sentiment Model

5Citations
Citations of this article
69Readers
Mendeley users who have this article in their library.

Abstract

This paper describes the NeuroSent system that participated in SemEval 2018 Task 3. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.

Cite

CITATION STYLE

APA

Dragoni, M. (2018). NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks through a Multi-Domain Sentiment Model. In NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop (pp. 512–519). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s18-1083

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free