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We use two datasets - (1) NEWS consisting of 5,880 news stories and 60K comments from four social media platforms: Twitter, Instagram, YouTube, and Facebook; and (2) IMDB consisting of 7,500 positive and 7,500 negative movie reviews - to investigate the agreement and bias of four widely used sentiment analysis (SA) tools: Microsoft Azure (MS), IBM Watson, Google Cloud, and Amazon Web Services (AWS). We find that the four tools assign the same sentiment on less than half (48.1%) of the analyzed content. We also find that AWS exhibits neutrality bias in both datasets, Google exhibits bi-polarity bias in the NEWS dataset but neutrality bias in the IMDB dataset, and IBM and MS exhibit no clear bias in the NEWS dataset but have bi-polarity bias in the IMDB dataset. Overall, IBM has the highest accuracy relative to the known ground truth in the IMDB dataset. Findings indicate that psycholinguistic features - especially affect, tone, and use of adjectives - explain why the tools disagree. Engineers are urged caution when implementing SA tools for applications, as the tool selection affects the obtained sentiment labels.<\/jats:p>","DOI":"10.1145\/3532203","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T17:36:27Z","timestamp":1655487387000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment: Analyzing the Sentiment Bias of Four Major Tools"],"prefix":"10.1145","volume":"6","author":[{"given":"Soon-Gyo","family":"Jung","sequence":"first","affiliation":[{"name":"Hamad bin Khalifa University, Doha, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joni","family":"Salminen","sequence":"additional","affiliation":[{"name":"University of Vaasa, Vaasa, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernard J.","family":"Jansen","sequence":"additional","affiliation":[{"name":"Hamad bin Khalifa University, Doha, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"volume-title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges. 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