Work

HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection

Year: 2018

Type: conference-paper

Abstract: This paper proposes a novel feature extraction process for SemEval task 3: Irony detection in English tweets.The proposed system incorporates a concatenative featurization of tweet and hashtags, which helps distinguishing between the irony-related and the other components.The system embeds tweets into a vector sequence with widely used pretrained word vectors, partially using a character embedding for the words that are out of vocabulary.Identification was performed with BiLSTM and CNN classifiers, achieving F1 score of 0.5939 (23/42) and 0.3925 (10/28) each for the binary and the multi-class case, respectively.The reliability of the proposed scheme was verified by analyzing the Gold test data, which demonstrates how hashtags can be taken into account when identifying various types of irony.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/s18-1089

Authors: Won Ik Cho, Woo Hyun Kang, Nam Soo Kim

Institution: Seoul National University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Ministry of Trade, Industry and Energy

Award: G7779203582