Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

This article presents a deep learning based method for determining the author's personality type from text: given a text, the presence or absence of the Big Five traits is detected in the author's psychological profile. For each of the five traits, the authors train a separate binary classifier, with identical architecture, based on a novel document modeling technique. Namely, the classifier is implemented as a specially designed deep convolutional neural network, with injection of the document-level Mairesse features, extracted directly from the text, into an inner layer. The first layers of the network treat each sentence of the text separately; then the sentences are aggregated into the document vector. Filtering out emotionally neutral input sentences improved the performance. This method outperformed the state of the art for all five traits, and the implementation is freely available for research purposes.

Although most works approach it as a simple categorization problem, sentiment analysis is actually a suitcase research problem that requires tackling many natural language processing (NLP) tasks. The expression “sentiment analysis” itself is a big suitcase (like many others related to affective computing, such as emotion recognition or opinion mining) that all of us use to encapsulate our jumbled idea about how our minds convey emotions and opinions through natural language. The authors address the composite nature of the problem via a three-layer structure inspired by the “jumping NLP curves” paradigm. In particular, they argue that there are (at least) 15 NLP problems that need to be solved to achieve human-like performance in sentiment analysis.