  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.
