Sentiment analysis is the computational study of opinions given by the users of online media platforms e.g. Twitter, Facebook, Instagram. The output will be in the form of polarity: positive, negative or indifferent. The field has become very useful for the industry as it can feed them the information of what is sought after by their customers in a given time. It has also rapidly became a topic of interest in the research world, for its importance and subjectivity. One of the most challenging issue in sentiment analysis is sarcasm. The existence of sarcasm is mostly ignored by the researchers in the field of sentiment analysis as it is considered to be too complex. Sarcasm is what most researchers regarded as a subset of irony. It is the utterance of positive statement with negative intent. Intent is hard to detect not only for computers but also for humans. The listener is deemed to have a certain degree of background knowledge or context of what the speaker is saying to understand sarcasm. The researches that takes sarcasm into account or solely focuses on sarcasm is in the trend of using context outside the target word for sarcasm detection, and the most popular approach is deep learning. However, both deep learning and context need a lot of features. In this paper, we will look at some researches that focuses on sarcasm detection and their agreement that more than text is needed to properly detect sarcasm. Also in this paper is the trends undergone by sarcasm detection researchers and their proposed techniques.

Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not really significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1measure. The combination of features are classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the performance of each feature set are also shown as additional information.