TY - GEN

T1 - Sentiment Analysis of Posts and Comments in the Accounts of Russian Politicians on the Social Network

AU - Svetlov, Kirill

AU - Platonov, Konstantin

N1 - Funding Information: This work was funded by RFBR and EISR, project number 19-011-31651. We would like to thank our colleagues from the Center for Sociological and Internet Research of Saint Petersburg State University for guidance and support. Publisher Copyright: © 2019 FRUCT. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2019/11

Y1 - 2019/11

N2 - Russian politicians are increasingly using social networks publishing a lot of texts. One of the important issues in the context of the analysis of political online communication is the choice of negative and positive topics in publications as well as the reaction of the audience. In order to analyze the main patterns of this process we have collected the data from the social network Vkontakte. Our sample covers the period from 1 January, 2017 to 25 April, 2019, in total 46293 posts and 2197063 comments in 23 politician's accounts. To build the classifier we used two text corpora: Rubtsova's corpus and RuSentiment corpus. The algorithm of sentiment analysis was implemented on the basis of bidirectional recurrent neural network. Using Rubtsova's corpus we provided the accuracy of 91% and using RuSentiment we provided the accuracy of 84% (accuracy is calculated as the proportion of correctly identified cases from the test sample). We found that the markup of data significantly differs when different corpora were used. The most adequate results in the analysis of posts and comments, in our opinion, are obtained by using an ensemble of models based on the both corpora. As a result of classification, we identified a number of patterns. Thus, the number of likes and views of posts is higher for the posts classified as positive, and the number of reposts is higher for the posts classified as negative. We also found that the number of comments is higher for the posts with a negative sentiment, and the average sentiment of comments on positive posts is more positive than the average sentiment of comments on negative posts.

AB - Russian politicians are increasingly using social networks publishing a lot of texts. One of the important issues in the context of the analysis of political online communication is the choice of negative and positive topics in publications as well as the reaction of the audience. In order to analyze the main patterns of this process we have collected the data from the social network Vkontakte. Our sample covers the period from 1 January, 2017 to 25 April, 2019, in total 46293 posts and 2197063 comments in 23 politician's accounts. To build the classifier we used two text corpora: Rubtsova's corpus and RuSentiment corpus. The algorithm of sentiment analysis was implemented on the basis of bidirectional recurrent neural network. Using Rubtsova's corpus we provided the accuracy of 91% and using RuSentiment we provided the accuracy of 84% (accuracy is calculated as the proportion of correctly identified cases from the test sample). We found that the markup of data significantly differs when different corpora were used. The most adequate results in the analysis of posts and comments, in our opinion, are obtained by using an ensemble of models based on the both corpora. As a result of classification, we identified a number of patterns. Thus, the number of likes and views of posts is higher for the posts classified as positive, and the number of reposts is higher for the posts classified as negative. We also found that the number of comments is higher for the posts with a negative sentiment, and the average sentiment of comments on positive posts is more positive than the average sentiment of comments on negative posts.

UR - http://www.scopus.com/inward/record.url?scp=85080856633&partnerID=8YFLogxK

U2 - 10.23919/FRUCT48121.2019.8981501

DO - 10.23919/FRUCT48121.2019.8981501

M3 - Conference contribution

AN - SCOPUS:85080856633

T3 - Conference of Open Innovation Association, FRUCT

SP - 299

EP - 305

BT - Proceedings of the 25th Conference of Open Innovations Association FRUCT, FRUCT 2019

A2 - Niemi, Valtteri

A2 - Tyutina, Tatiana

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th Conference of Open Innovations Association FRUCT, FRUCT 2019

Y2 - 5 November 2019 through 8 November 2019

ER -