Social media is abundant in visual and textual information presented together\nor in isolation. Memes are the most popular form, belonging to the former\nclass. In this paper, we present our approaches for the Memotion Analysis\nproblem as posed in SemEval-2020 Task 8. The goal of this task is to classify\nmemes based on their emotional content and sentiment. We leverage techniques\nfrom Natural Language Processing (NLP) and Computer Vision (CV) towards the\nsentiment classification of internet memes (Subtask A). We consider Bimodal\n(text and image) as well as Unimodal (text-only) techniques in our study\nranging from the Na\\"ive Bayes classifier to Transformer-based approaches. Our\nresults show that a text-only approach, a simple Feed Forward Neural Network\n(FFNN) with Word2vec embeddings as input, performs superior to all the others.\nWe stand first in the Sentiment analysis task with a relative improvement of\n63% over the baseline macro-F1 score. Our work is relevant to any task\nconcerned with the combination of different modalities.\n