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Social media is abundant in visual and textual information presented togetheror in isolation. Memes are the most popular form, belonging to the formerclass. In this paper, we present our approaches for the Memotion Analysisproblem as posed in SemEval-2020 Task 8. The goal of this task is to classifymemes based on their emotional content and sentiment. We leverage techniquesfrom Natural Language Processing (NLP) and Computer Vision (CV) towards thesentiment classification of internet memes (Subtask A). We consider Bimodal(text and image) as well as Unimodal (text-only) techniques in our studyranging from the Na\"ive Bayes classifier to Transformer-based approaches. Ourresults 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 of63% over the baseline macro-F1 score. Our work is relevant to any taskconcerned with the combination of different modalities.

IITK at SemEval-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of Internet Memes