Sarcasm identification is a confined research area in NLP, a specific case of opinion mining where the focal point in the process is identification of sarcasm, instead of sentiment extraction. Sarcasm is a specific type of opinion which is expressed as a negative feeling in form of anger, frustration, or derision veiled by the intense positive words in the text. Detection of sarcasm, which is an elusive problem for machines, has gained wide popularity in the research community in recent years. Accurate identification and analysis of sarcasm improves the performance of sentiment identification models. This manuscript details various sarcasm detection approaches, models and features used, issues, challenges, and further research scope. The various machine learning and deep learning models used to identify sarcasm are detailed in this script.

Music genre identification is crucial for the classification and recommendation of songs in music applications. Manually labelling songs takes up a significant amount of time. In this paper, we propose a deep learning model to automate the process of genre identification. The process mainly involves three steps: preprocessing the dataset to get a simplified version of each song, building a deep neural network, and training and using it to predict the genre of songs. Input to the model is Mel-frequency Cepstral Coefficient (MFCC) values of the audio files from the GTZAN dataset that consists of 10 different genres. After training, the model produced a result of 60% accuracy. Observing the actual and predicted values, the model seemed to exhibit overfitting. To overcome this, we used dropouts and regularization in the model, followed by early stopping, which gave a final accuracy of 67.5%.