NDLI: Sentiment-Based Features for Predicting Election Polls: A Case Study on the Brazilian Scenario

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Sentiment-Based Features for Predicting Election Polls: A Case Study on the Brazilian Scenario

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Sentiment-Based Features for Predicting Election Polls: A Case Study on the Brazilian Scenario

Content Provider	ACM Digital Library
Author	Tumitan, Diego Becker, Karin
Abstract	The success of opinion mining for automatically processing vast amounts of opinionated content available on the Internet has been demonstrated as a less expensive and lower latency solution for gathering public opinion. In this paper, we investigate whether it is possible to predict variations in vote intention based on sentiment time series extracted from news comments, using three Brazilian elections as case study. The contributions of this case study are: a) the comparison of two approaches for opinion mining in user-generated content in Brazilian Portuguese, b) the proposition of two types of features to represent sentiment behavior towards political candidates that can be used for prediction, c) an approach to predict polls vote intention variations that is adequate for scenarios of sparse data. We developed experiments to assess the influence on the forecasting accuracy of the proposed features, and their respective preparation. Our results display an accuracy of 70% in predicting positive and negative variations. These are important contributions towards a more general framework that is able to blend opinions from several different sources to find representativeness of the target population, and make more reliable predictions.
Starting Page	126
Ending Page	133
Page Count	8
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.89
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-11
Access Restriction	Subscribed
Subject Keyword	Opinion mining user-generated content sentiment-based prediction
Content Type	Text
Resource Type	Article

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