Paper Type

Complete

Paper Number

1462

Description

The accuracy of different cloud-based technologies for sentiment analysis may vary based on attributes such as the length of the analyzed texts and the dominant sentiment in a corpus. A potential strategy to reduce the variability in accuracy is to create ensemble models formed by individual technologies. Our goal in this paper is to study the performance of different ensembles of cloud-based technologies for sentiment analysis. Overall, we find that ensemble models perform better on long texts, a scenario where individual technologies tend to struggle. We also find that score-based ensembles perform better than label-based ensembles. Besides being of value to practitioners, we discuss how our results might increase the reliability of research findings that rely on sentiment analysis. In particular, we argue that ensemble models may reduce the chances of sentiment-dependent results being driven by a single technology.

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Aug 9th, 12:00 AM

Studies on the Accuracy of Ensembles of Cloud-Based Technologies for Sentiment Analysis

The accuracy of different cloud-based technologies for sentiment analysis may vary based on attributes such as the length of the analyzed texts and the dominant sentiment in a corpus. A potential strategy to reduce the variability in accuracy is to create ensemble models formed by individual technologies. Our goal in this paper is to study the performance of different ensembles of cloud-based technologies for sentiment analysis. Overall, we find that ensemble models perform better on long texts, a scenario where individual technologies tend to struggle. We also find that score-based ensembles perform better than label-based ensembles. Besides being of value to practitioners, we discuss how our results might increase the reliability of research findings that rely on sentiment analysis. In particular, we argue that ensemble models may reduce the chances of sentiment-dependent results being driven by a single technology.

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