Biblio

Sentiment analytics is the process of applying natural language processing and methods for text-based information to define and extract subjective knowledge of the text. Natural language processing and text classifications can deal with limited corpus data and more attention has been gained by semantic texts and word embedding methods. Deep learning is a powerful method that learns different layers of representations or qualities of information and produces state-of-the-art prediction results. In different applications of sentiment analytics, deep learning methods are used at the sentence, document, and aspect levels. This review paper is based on the main difficulties in the sentiment assessment stage that significantly affect sentiment score, pooling, and polarity detection. The most popular deep learning methods are a Convolution Neural Network and Recurrent Neural Network. Finally, a comparative study is made with a vast literature survey using deep learning models.

In the past three years, Emotion Recognition in the Wild (EmotiW) Grand Challenge has drawn more and more attention due to its huge potential applications. In the fourth challenge, aimed at the task of video based emotion recognition, we propose a multi-clue emotion fusion (MCEF) framework by modeling human emotion from three mutually complementary sources, facial appearance texture, facial action, and audio. To extract high-level emotion features from sequential face images, we employ a CNN-RNN architecture, where face image from each frame is first fed into the fine-tuned VGG-Face network to extract face feature, and then the features of all frames are sequentially traversed in a bidirectional RNN so as to capture dynamic changes of facial textures. To attain more accurate facial actions, a facial landmark trajectory model is proposed to explicitly learn emotion variations of facial components. Further, audio signals are also modeled in a CNN framework by extracting low-level energy features from segmented audio clips and then stacking them as an image-like map. Finally, we fuse the results generated from three clues to boost the performance of emotion recognition. Our proposed MCEF achieves an overall accuracy of 56.66% with a large improvement of 16.19% with respect to the baseline.