{"title":"基于注意的文本-图像对多模态情感识别模型","authors":"Ananya Pandey, D. Vishwakarma","doi":"10.1109/ICITIIT57246.2023.10068626","DOIUrl":null,"url":null,"abstract":"Multi-modal sentiment recognition (MSR) is an emerging classification task that aims to categorize sentiment polarities for a given multi-modal dataset. The majority of work done in the past relied heavily on text-based information. However, in many scenarios, text alone is frequently insufficient to predict sentiment accurately; as a result, academics are more motivated to engage in the subject of MSR. In light of this, we proposed an attention-based model for MSR using image-text pairs of tweets. To effectively capture the vital information from both modalities, our approach combines BERT and ConvNet with CBAM (convolution block attention module) attention. The outcomes of our experimentations on the Twitter-17 dataset demonstrate that our method is capable of sentiment classification accuracy that is superior to that of competing approaches.","PeriodicalId":170485,"journal":{"name":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Attention-based Model for Multi-modal sentiment recognition using Text-Image Pairs\",\"authors\":\"Ananya Pandey, D. Vishwakarma\",\"doi\":\"10.1109/ICITIIT57246.2023.10068626\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-modal sentiment recognition (MSR) is an emerging classification task that aims to categorize sentiment polarities for a given multi-modal dataset. The majority of work done in the past relied heavily on text-based information. However, in many scenarios, text alone is frequently insufficient to predict sentiment accurately; as a result, academics are more motivated to engage in the subject of MSR. In light of this, we proposed an attention-based model for MSR using image-text pairs of tweets. To effectively capture the vital information from both modalities, our approach combines BERT and ConvNet with CBAM (convolution block attention module) attention. The outcomes of our experimentations on the Twitter-17 dataset demonstrate that our method is capable of sentiment classification accuracy that is superior to that of competing approaches.\",\"PeriodicalId\":170485,\"journal\":{\"name\":\"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)\",\"volume\":\"222 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICITIIT57246.2023.10068626\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITIIT57246.2023.10068626","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Attention-based Model for Multi-modal sentiment recognition using Text-Image Pairs
Multi-modal sentiment recognition (MSR) is an emerging classification task that aims to categorize sentiment polarities for a given multi-modal dataset. The majority of work done in the past relied heavily on text-based information. However, in many scenarios, text alone is frequently insufficient to predict sentiment accurately; as a result, academics are more motivated to engage in the subject of MSR. In light of this, we proposed an attention-based model for MSR using image-text pairs of tweets. To effectively capture the vital information from both modalities, our approach combines BERT and ConvNet with CBAM (convolution block attention module) attention. The outcomes of our experimentations on the Twitter-17 dataset demonstrate that our method is capable of sentiment classification accuracy that is superior to that of competing approaches.