论文标题
IITK在2020 Semeval-2020任务8：互联网模因的单峰和双峰情绪分析
IITK at SemEval-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of Internet Memes
论文作者
论文摘要
社交媒体在一起或孤立的视觉和文本信息中很丰富。模因是最受欢迎的形式，属于以前的阶级。在本文中，我们介绍了Semeval-2020 Task 8中提出的Memotion分析问题的方法。该任务的目的是根据他们的情感内容和情感对模因进行分类。我们利用自然语言处理（NLP）和计算机愿景（CV）的技术到互联网模因的情感分类（子任务A）。我们在研究中考虑了双峰（文本和图像）以及单峰（仅文本）技术，从幼稚的贝叶斯分类器到基于变压器的方法。我们的结果表明，只有文本方法是一种简单的feed向前神经网络（FFNN），其word2vec嵌入为输入，其性能优于其他所有功能。我们在情感分析任务中首先站稳脚跟，比基线宏F1分数的相对提高63％。我们的工作与与不同方式的结合有关的任何任务都相关。
Social media is abundant in visual and textual information presented together or in isolation. Memes are the most popular form, belonging to the former class. In this paper, we present our approaches for the Memotion Analysis problem as posed in SemEval-2020 Task 8. The goal of this task is to classify memes based on their emotional content and sentiment. We leverage techniques from Natural Language Processing (NLP) and Computer Vision (CV) towards the sentiment classification of internet memes (Subtask A). We consider Bimodal (text and image) as well as Unimodal (text-only) techniques in our study ranging from the Naïve Bayes classifier to Transformer-based approaches. Our results show that a text-only approach, a simple Feed Forward Neural Network (FFNN) with Word2vec embeddings as input, performs superior to all the others. We stand first in the Sentiment analysis task with a relative improvement of 63% over the baseline macro-F1 score. Our work is relevant to any task concerned with the combination of different modalities.
