2014Unpublished venueOpen access

Benchmarking Twitter Sentiment Analysis Tools

Ahmed Abbasi, Ammar Hassan, Milan Dhar

Open full text 65 citations

Abstract

Twitter has become one of the quintessential social media platforms for user-generated content.Researchers and industry practitioners are increasingly interested in Twitter sentiments.Consequently, an array of commercial and freely available Twitter sentiment analysis tools have emerged, though it remains unclear how well these tools really work.This study presents the findings of a detailed benchmark analysis of Twitter sentiment analysis tools, incorporating 20 tools applied to 5 different test beds.In addition to presenting detailed performance evaluation results, a thorough error analysis is used to highlight the most prevalent challenges facing Twitter sentiment analysis tools.The results have important implications for various stakeholder groups, including social media analytics researchers, NLP developers, and industry managers and practitioners using social media sentiments as input for decision-making.

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What this paper is about

Twitter has become one of the quintessential social media platforms for user-generated content.Researchers and industry practitioners are increasingly interested in Twitter sentiments.Consequently, an array of commercial and freely available Twitter sentiment analysis tools have emerged, though it remains unclear how well these tools really work.This study presents the findings of a detailed benchmark analysis of Twitter sentiment analysis tools, incorporating 20 tools applied to 5 different test beds.In addition to presenting detailed performance evaluation results, a thorough error analysis is used to highlight the most prevalent challenges facing Twitter sentiment analysis tools.The results have important implications for various stakeholder groups, including social media analytics researchers, NLP developers, and industry managers and practitioners using social media sentiments as input for decision-making.

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Available abstract

Twitter has become one of the quintessential social media platforms for user-generated content.Researchers and industry practitioners are increasingly interested in Twitter sentiments.Consequently, an array of commercial and freely available Twitter sentiment analysis tools have emerged, though it remains unclear how well these tools really work.This study presents the findings of a detailed benchmark analysis of Twitter sentiment analysis tools, incorporating 20 tools applied to 5 different test beds.In addition to presenting detailed performance evaluation results, a thorough error analysis is used to highlight the most prevalent challenges facing Twitter sentiment analysis tools.The results have important implications for various stakeholder groups, including social media analytics researchers, NLP developers, and industry managers and practitioners using social media sentiments as input for decision-making.

Key concepts: Sentiment analysis, Social media, Benchmarking, Social media analytics, Computer science, Data science, Benchmark (surveying), Analytics

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