MCNN: Visual Sentiment Analysis using Various Deep Learning Framework with Deep CNN
Keywords:
Visual Sentiment analysis, CNN, ResNet50, VGG16, Deep LearningAbstract
Sentiment analysis is a technique for assessing people's opinions and viewpoints based on the text or photos they publish on social networking sites such as Instagram and Twitter. Because it can be difficult to pinpoint the precise thoughts, ideas, and sentiments expressed in a text document or image, sentiment classification is a difficult undertaking. People share their emotions in diverse ways depending on the situation and subject. In this paper, we proposed a hybrid feature extraction and selection technique using numerous deep learning techniques. The features extracted from the image, such as luminance, chrominance, histogram, autoencoder, etc., are validated with a modified convolutional neural network called mCNN. The number of deep CNN layers, size of extracted features, various activation functions, and different optimizers have been used for CNN feeding. In an extensive experimental analysis, our module was tested and compared with two different deep learning modules, such as RESNET and VGGNET. Our proposed module obtains higher accuracy than two conventional deep learning frameworks.
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Yun Liang, Keisuke Maeda, Takahiro Ogawa and Miki Haseyama. “Deep Metric Network via Heterogeneous Semantics for image Sentiment Analysis”, 2021, International Conference on Image Processing (ICIP), IEEE.
Jie Xu, Zhoujun Li, Feiran Huang, Chaozhuo Li, and Philip S.Yu. “Social Image Sentiment Analysis by Exploiting Multimodal Content and Heterogeneous Relations”, 2020, IEEE.
Yingying Pan, Ruimin Lyu, Qinyan Nie and Lei Meng. “Study on the Emotional Image of Calligraphy Strokes based on Sentiment Analysis”, 2020, IEEE.
Junfeng Yao, Yao Yu, and Xiaoling Xue. “Sentiment Prediction in Scene Images via Convolutional Neural Networks”, 2016, 31st Youth Academic Annual Conference of Chinese Association of Automation, IEEE.
Stuti Jindal and Sanjay Singh. “Image Sentiment Analysis using Deep Convolutional Neural Networks with Domain Specific Fine Tuning”, 2015, International Conference on Information Processing (ICIP), IEEE.
Igor Santos, Nadia Nedjah and Luiza de Macedo Mourelle. “Sentiment Analysis using Convolutional Neural Network with fastText Embeddings”, 2017, IEEE.
Sani Kamış and Dionysis Goularas. “Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data”, 2019, International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), IEEE.
Lifang wu, Shuang Liu, Meng Jian, Jiebo Luo, Xiuzhen Zhang and Mingchao Qi. “Reducing Noisy Labels in Weakly Labeled Data for Visual Sentiment Analysis”, 2017, IEEE.
Selvarajah Thuseethan, Sivasubramaniam Janarthan, Sutharshan Rajasegarar, Priya Kumari and John Yearwood. “Multimodal Deep Learning Framework for Sentiment Analysis from Text-Image Web Data”, 2020, WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE.
Jiajie Tang, Liandong Fu, Chong Tan and Mingjun Peng. “Research on Sentiment Classification of Active Scene Images Based on DNN”, 2019, International Conference on Virtual Reality and Intelligent Systems (ICVRIS), IEEE.
Monika Saini and Mala Kalra. “An Enhanced Convolution Neural Network Based Approach for Classification of Sentiments”, 2020, International Conference for Emerging Technology (INCET), IEEE.
Theodoros Giannakopoulos, Michalis Papakostas, Stavros Perantonis and Vangelis Karkaletsis. “Visual sentiment analysis for brand monitoring enhancement”, 2015, 9th International Symposium on Image and Signal Processing and Analysis (ISPA), IEEE.
Jufeng Yang, Dongyu She, Ming Sun, Ming-Ming Cheng, Paul L. Rosin and Liang Wang. “Visual Sentiment Prediction based on Automatic Discovery of Affective Regions”, 2018, IEEE.
Dr. Munish Mehta, Kanhav Gupta, Shubhangi Tiwari and Anamika. “A Review on Sentiment Analysis of Text, Image and Audio Data”, 2021, 5th International Conference on Computing Methodologies and Communication (ICCMC), IEEE.
Dongyu She, Jufeng Yang, Ming-Ming Cheng, Yu-Kun Lai, Paul L. Rosin and Liang Wang. “WSCNet: Weakly Supervised Coupled Networks for Visual Sentiment Classification and Detection”, 2019, IEEE.
Jing Zhang, Han Sun, Zhe Wang and Tong Ruan. “Another Dimension: Towards Multi-Subnet Neural Network for Image Sentiment Analysis”, 2019, international Conference on Multimedia and Expo (ICME), IEEE.
Xingxu Yao, Dongyu She, Haiwei Zhang, Jufeng Yang, Ming-Ming Cheng and Liang Wang. “Adaptive Deep Metric Learning for Affective Image Retrieval and Classification”, 2020, IEEE.
Jianfei Yu, Jing Jiang and Rui Xia. “Entity-Sensitive Attention and Fusion Network for Entity-Level Multimodal Sentiment Classification”, 2019, IEEE.
Pooyan Balouchian, Marjaneh Safaei and Hassan Foroosh. “LUCFER: A Large-Scale Context-Sensitive Image Dataset for Deep Learning of Visual Emotions”, 2019, Winter Conference on Applications of Computer Vision, IEEE.
Macario O. Cordel, Shaojing Fan, Zhiqi Shen and Mohan S. Kankanhalli. “Emotion-Aware Human Attention Prediction”, 2019, CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.
Ya-Fang Peng and Tzren-Ru Chou. “Automatic Color Palette Design Using Color Image and Sentiment Analysis”, 2019, 4th International Conference on Cloud Computing and Big Data Analytics, IEEE.
Hakan Bilen and Andrea Vedaldi. “Weakly Supervised Deep Detection Networks”, 2016, Conference on Computer Vision and Pattern Recognition, IEEE.
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva and Antonio Torralba. “Learning Deep Features for Discriminative Localization”, 2016, Conference on Computer Vision and Pattern Recognition, IEEE.
Xavier Alameda-Pineda, Andrea Pilzer, Dan Xu, Nicu Sebe and Elisa Ricci. “Viraliency: Pooling Local Virality”, 2017, Conference on Computer Vision and Pattern Recognition, IEEE.
Ronak Kosti, Jose M. Alvarez, Adria Recasens and Agata Lapedriza. “Emotion Recognition in Context”, 2017, Conference on Computer Vision and Pattern Recognition, IEEE.
Ali Diba, Vivek Sharma, Ali Pazandeh, Hamed Pirsiavash and Luc Van Gool. “Weakly Supervised Cascaded Convolutional Networks”, 2017, Conference on Computer Vision and Pattern Recognition, IEEE.
Thibaut Durand, Taylor Mordan, Nicolas Thome and Matthieu Cord. “WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation”, 2017, Conference on Computer Vision and Pattern Recognition, IEEE.
Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao and Shuicheng Yan. “Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach”, 2017, Conference on Computer Vision and Pattern Recognition, IEEE.
V. Yanulevskaya, J.C. van Gemert, K. Roth, A.K. Herbold, N. Sebe, and J.M. Geusebroek. “Emotional Valence Categorization using Holistic image Features”, 2008, IEEE.
Kuan-Chuan Peng, Amir Sadovnik, Andrew Gallagher and Tsuhan Chen. “Where do Emotions come from? Predicting the Emotion Stimuli Map”, 2016, IEEE.
Ramazan Gokberk Cinbis, Jakob Verbeek, and Cordelia Schmid. “Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning”, 2015, Transactions on Pattern Analysis and Machine Intelligence, IEEE.
Kaiqi Huang, Weiqiang Ren, Dacheng Tao and Tieniu Tan. “On Combining Multiple Instance Linear SVM and Bag Splitting for High Performance Visual Object Localization”, 2015, Transactions on Pattern Analysis and Machine Intelligence, IEEE.
Bogdan Alexe, Thomas Deselaers and Vittorio Ferrari. “Measuring the Objectness of Image Windows”, 2012, Transactions on Pattern Analysis and Machine Intelligence, IEEE.
Sicheng Zhao, Hongxun Yao, Yue Gao, Rongrong Ji and Guiguang Ding. “Continuous Probability Distribution Prediction of Image Emotions via Multi-Task Shared Sparse Regression”, 2016, Transactions on Multimedia, IEEE.
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