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Information 2018, 9(8), 184; https://doi.org/10.3390/info9080184

Reducing the Deterioration of Sentiment Analysis Results Due to the Time Impact

Institute of Informatics Systems, Novosibirsk State University, 630090 Novosibirsk, Russia
This manuscript is an extended version of our paper ”Reducing the degradation of sentiment analysis for text collections spread over a period of time” published in the proceedings of Knowledge Engineering and Semantic Web, Szczecin, Poland, 8–10 November 2017.
Received: 24 June 2018 / Revised: 16 July 2018 / Accepted: 24 July 2018 / Published: 25 July 2018
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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Abstract

The research identifies and substantiates the problem of quality deterioration in the sentiment classification of text collections identical in composition and characteristics, but staggered over time. It is shown that the quality of sentiment classification can drop up to 15% in terms of the F-measure over a year and a half. This paper presents three different approaches to improving text classification by sentiment in continuously-updated text collections in Russian: using a weighing scheme with linear computational complexity, adding lexicons of emotional vocabulary to the feature space and distributed word representation. All methods are compared, and it is shown which method is most applicable in certain cases. Experiments comparing the methods on sufficiently representative text collections are described. It is shown that suggested approaches could reduce the deterioration of sentiment classification results for collections staggered over time. View Full-Text
Keywords: sentiment classification; text classification; machine learning; sentiment analysis; social network analysis sentiment classification; text classification; machine learning; sentiment analysis; social network analysis
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Rubtsova, Y. Reducing the Deterioration of Sentiment Analysis Results Due to the Time Impact. Information 2018, 9, 184.

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