TY - JOUR

T1 - Reducing the deterioration of sentiment analysis results due to the time impact

AU - Rubtsova, Yuliya

PY - 2018/7/25

Y1 - 2018/7/25

N2 - 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.

AB - 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.

KW - Machine learning

KW - Sentiment analysis

KW - Sentiment classification

KW - Social network analysis

KW - Text classification

KW - sentiment classification

KW - sentiment analysis

KW - social network analysis

KW - text classification

KW - machine learning

UR - http://www.scopus.com/inward/record.url?scp=85052712524&partnerID=8YFLogxK

U2 - 10.3390/info9080184

DO - 10.3390/info9080184

M3 - Article

AN - SCOPUS:85052712524

VL - 9

JO - Information (Switzerland)

JF - Information (Switzerland)

SN - 2078-2489

IS - 8

M1 - 184

ER -