NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture

Abstract

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.

Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

“…A team named QCRI proposed ordinal tree for task E and for task D compared already proposed methods from literature for binary quantification [27]. Another team named ISTI-CNR also participated in SemEval-2016 for all tasks [28]. For ordinal quantification of data, Regress and Count method is proposed while for adjusted data Adjusted Regress and Count method is proposed.…”

“…Sentiment quantification has also been applied on content other than English, for instance, the research study [29] uses Arabic language content. A team named NRU-HSE [28] used LSTM for classification of data for task D and the results were compared with that was proposed in 2016 in terms of KLD. Another team named TwiSE [29] also participated in SemEval-2017.…”