PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks.

Abstract

This paper presents an alternative approach to polarity and intensity classification of sentiments in microblogs. In contrast to previous works, which either relied on carefully designed hand-crafted feature sets or automatically derived neural embeddings for words, our method harnesses character embeddings as its main input units. We obtain task-specific vector representations of characters by training a deep multi-layer convolutional neural network on the labeled dataset provided to the participants of the SemEval-2016 Shared Task 4 (Sentiment Analysis in Twitter; Nakov et al., 2016b) and subsequently evaluate our classifiers on subtasks B (two-way polarity classification) and C (joint five-way prediction of polarity and intensity) of this competition. Our first system, which uses three manifold convolution sets followed by four non-linear layers, ranks 16 in the former track; while our second network, which consists of a single convolutional filter set followed by a highway layer and three non-linearities with linear mappings in-between, attains the 10-th place on subtask C.

Domain Adaptation of Transformer-Based Models Using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback

“…Sidarenka [32] developed three systems for the second subtask. The first was an SVM classifier trained on a variety of different features such as character-level features, wordlevel features, part-of-speech features, and lexicons features.…”