5/16/2023 0 Comments Smedium meme![]() ![]() , who reported the best-performing sentiment classification solution to the Memotion 1.0 dataset, showed that the same architecture outperforms all, or all but one, competing solution when individually trained on eight affect dimensions.Ī typical approach to building a multimodal meme classifier is to generate unimodal representations of each modality before fusing these representations into a multimodal representation of the meme, such as in. Their findings suggest that meme classification architectures exhibit adaptability across different affective computing tasks. Most apparently, Bucur et al.’s winning submission of the Memotion 2022 Challenge, was trained to simultaneously classify sentiment polarity, offensiveness, sarcasm, humour, and motivational intent. Therefore, meme classifiers must be able to learn subtle intermodal relationships with very limited input.Īrchitecturally, the current literature suggests that various affective classification tasks can be applied to memes without requiring entirely distinct approaches. Furthermore, slight changes in either modality can change a hateful meme into a harmless one and vice versa. show how harmless images and texts could be combined to create hateful memes. Second, memes use short text pieces and few foreground visual objects, relying on intermodal relations to convey meaning. Text in memes is often intentionally located amongst other visual content to create meaning. ![]() ![]() First, the text and image of a meme share a common visual medium, unlike the more common image-caption pairs. Memes are distinct from other multimodal user-generated content types in several key ways. they consider the position of a word amongst text but not its position in relation to the meme image or vice versa. Current approaches that use positional information in meme sentiment classification opt to omit intermodal positional relations, i.e. 1c) text clusters can be paired with image segments, with each pair signifying a different sentiment (e.g., Fig. Meme authors intentionally position a grouping of words (“text clusters”) to convey meaning, such as implying hateful analogies (e.g., Fig. Unlike many other forms of multimodal content, the text within a meme is interspersed into its image, often either superimposed on the image or comprising a segment of the meme image, creating a shared visual medium. Recent work has shown that incorporating additional relevant information improves the performance of meme affective classifiers, amongst which is positional information of words within text and visual objects within an image. The breadth of this challenge spans various affective goals, including sentiment polarity, offensiveness, sarcasm, and motivational intent. Thus, solutions must consider the semantics of each, the textual and visual modalities, and their combinations. Memes are challenging input in automated affective classification problems, as they typically exhibit very brief texts, references to popular culture, subtle intermodal semantic relations, and dependence on background context. This work contributes to the underlying problem of sentiment polarity classification of a meme: “Given a meme in a visual format, comprising an image I with embedded text T, classify the meme as having the overall sentiment of either Negative (e.g., Fig. Automated analysis of memes allows for: including memes in automated opinion mining processes, taking action against meme-based hate speech, identifying disinformation campaigns, and investigating social and political cultures. Memes are commonly found in various online communities to communicate ideas, incite humour, and express emotions. Along with the advent of other multimodal formats of user-generated content, Internet memes (or simply “memes”) have proliferated. ![]()
0 Comments
Leave a Reply. |