BennettNLP at SemEval-2020 Task 8: Multimodal sentiment classification Using Hybrid Hierarchical Classifier

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1085–1093 Barcelona, Spain (Online), December 12, 2020. 1085 BennettNLP at SemEval-2020 Task 8: Multimodal sentiment classification Using Hybrid Hierarchical Classifier Ambuje Gupta, Harsh Kataria, Souvik Mishra, Tapas Badal, Vipul Mishra* Department of computer science engineering Bennett University, Greater Noida, India (ambujegupta99, hkataria99, mishra.souvik911)@gmail.com (tapas.badal, vipul.mishra)@bennett.edu.in Abstract Memotion analysis is a very crucial and important subject in today’s world that is dominated by social media. This paper presents the results and analysis of the SemEval-2020 Task-8: Mem- otion analysis by team Kraken that qualified as winners for the task. This involved performing multimodal sentiment analysis on memes commonly posted over social media. The task com- prised of 3 subtasks, Task A was to find the overall sentiment of a meme and classify it into positive, negative or neutral, Task B was to classify it into the different types which were namely humour, sarcasm, offensive or motivation where a meme could have more than one category, Task C was to further quantify the classifications achieved in task B. An imbalanced data of 6992 rows was utilized for this which contained images (memes), text (extracted OCR) and their anno- tations in 17 classes provided by the task organisers. In this paper, the authors proposed a hybrid neural Naïve-Bayes Support Vector Machine and logistic regression to solve a multilevel 17 class classification problem. It achieved the best result in Task B i.e 0.70 F1 score. The authors were ranked third in Task B. 1 Introduction With the increased availability of internet and connected smart multimedia devices, there has been a rapid increase in the spreading of memes over social media platforms like Twitter, Facebook, Instagram, Reddit, etc. Memes are images containing text both of which are related to each other with a context. Memes usually pertain to an idea, belief, thought, theory, etc. which are usually derived from popular or unpopular culture with factors like place, people, events, and actions affecting it. These memes are an integral source of information about a community (Gal et al., 2016). This is why we require a thorough analysis of these memes. Not only that but also due to hate speech which has been the bane of social media since the beginning. These are the platforms where people from different parts of the world come together to interact and share ideas, such platforms encourage the concept of free speech however with this comes the issue of usage of offensive or anti-social content. Such content, which is not considered criminal, originates from the intent to harass in the name of criticism, leads to a radicalized person or community. Detecting such hate speech and offensive content to curb the spread of negativity and emotional distress using the internet is not only a priority task but a social responsibility. Such memes that cause trauma and torment to a directed person or a community needs to be flagged, reported, and taken proper action upon to avoid any unwanted disturbance to the people. …

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