IITK at SemEval-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of Internet Memes
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 Na\"ive 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.
Code (1)
Tasks
Sentiment AnalysisSentiment ClassificationSimilar Papers 제목 키워드 기반
ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach
Structured Sentiment Analysis (SSA) deals with extracting opinion tuples in a text, where each tuple (h, e, t, p) consists of h, the holder, who expresses a sentiment polarity p towards a target t through a sentiment exp…
DecoderSentenceSentiment AnalysisDSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis
Memes have become an ubiquitous social media entity and the processing and analysis of suchmultimodal data is currently an active area of research. This paper presents our work on theMemotion Analysis shared task of SemE…
ClassificationEmotion RecognitionGeneral ClassificationLightweight Models for Multimodal Sequential Data
Human language encompasses more than just text; it also conveys emotions through tone and gestures. We present a case study of three simple and efficient Transformer-based architectures for predicting sentiment and emoti…
Multimodal Sentiment AnalysisSentiment AnalysisIITK at SemEval-2020 Task 10: Transformers for Emphasis Selection
This paper describes the system proposed for addressing the research problem posed in Task 10 of SemEval-2020: Emphasis Selection For Written Text in Visual Media. We propose an end-to-end model that takes as input the t…
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search
In this paper, we propose the CodeRetriever model, which combines the unimodal and bimodal contrastive learning to train function-level code semantic representations, specifically for the code search task. For unimodal c…
Code SearchContrastive Learning