Detecting and analyzing emotions from human facial movement is a problem defined and developed over many years for the benefits it brings. Facial expression is a crux of the human-computer interaction (HCI) research area. Researchers are exploring its application in security, medical science, and to know the behavior of a person or community. In this paper, we have proposed a deep learning-based framework using transfer learning for facial expression. This approach uses the existing VGG16 model for a modified trained model and concatenates additional layers on it. VGG16 model is already trained on ImageNet, which has 1000 classes. After this, the model has been verified on CK+, JAFFE benchmark datasets. Extended Cohn-Kanade (CK+) and Japanese Female Facial Expression (JAFFE) are popular Facial Expression Dataset. The proposed model has shown 94.8% accuracy on CK+ and 93.7% on the JAFFE dataset and found superior to existing techniques. We have implemented proposed technique on Google Colab-GPU that has helped us to process these data.

The paper disccuses about the sentimenal analysis process which is a classification problem. To study the emotions of people about alternate energy sources, we carried out sentiment analysis on Twitter data. Sentiment analysis is used to analyze opinions of the user for decision making with the help of natural language processing techniques. In this paper, we carried out sentiment analysis and classification task of tweets belonging to #RenewableEnergy. We applied five different machine learning algorithms for the classification of tweets into three categories. We have carried classification without feature selection technique and with feature selection techniques. We have used CfsSubsetEvaluation and Information Gain feature selection methods to reduce the number of features from the dataset. The result obtained through the techniques followed in this paper, shows that the accuracy of sentiment classification is better with feature selection methods. The best accuracy (92.96%) is achieved with Support Vector Machine(Using PUK Kernel) and CfsSubsetEval feature selection method.