In many areas, such as social science, politics or market research, people need to deal with dataset shifting over time. Distribution drift phenomenon usually appears in the field of sentiment analysis, when proportions of instances are changing over time. In this case, the task is to correctly estimate proportions of each sentiment expressed in the set of documents (quantification task). Basically, our study was aimed to analyze the effectiveness of a mixture of quantification technique with one of deep learning architecture. All the techniques are evaluated using the SemEval-2017 Task4 dataset and source code, mentioned in this paper and available online in the Python programming language. The results of an application of the quantification techniques are discussed.
NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library
Nikolay Karpov | Alexander Porshnev | Kirill Rudakov
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)