Sentiment Analysis as a Service: A Social Media Based Sentiment Analysis Framework

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

“… Kim, et al (2016) TwitterNews Publications 16,189 news articles7,106,297 tweets Between 1 June 2014 and 31 August 2014 Ebola Sentiment Dynamics of the Hot Health Issue of Ebola Lexicon Based Models Chung, et al (2015) Twitter 255,118 tweets January 2015 Ebola Ebola Outbreak Discussions on Twitter Lexicon Based Models Deng, et al (2015) SinaTencentSohuNeteaseTwitter 140,000 tweets180,000 records June to November 2014 EbolaTyphoon HaiyanHagupit Distributed Mining System for Online Opinion Data Collecting and Mining. Lexicon Based Models Zarrad, et al (2014) Twitter 1,500,000 tweets 3 months MERS-CoV Addressing New Challenges for Big Data Platform with an Opinion Mining Approach Lexicon Based Models K. Ali, et al (2017) TwitterRedditInstagramNews Fora 525 Reviews 2 Months Outbreaks Locations Monitoring for Disease Outbreak Lexicon Based Models Almazidy, Althani, and Mohammed (2016) Twitter N/A N/A Outbreak Outbreak Notification Framework Using Twitter Mining for Smart Home Dashboards Lexicon Based Models Bhat, et al (2020) Twitter N/A N/A COVID-19 Sentiment analysis of Social Media Response on the COVID-19 Outbreak Lexicon Based Models Lwin, et al (2020) Twitter 20,325,929 tweets 28 January 2020 to 9 April 2020 COVID-19 Examining Worldwide Trends of Different Emotions During the COVID-19 Pandemic. Lexicon Based Models Raamkumar, Tan, and Wee (2020) Facebook 3,185,460 posts 1 January 2019 to 18 March 2020 COVID-...…”

“…Social media platforms are considered the global centre of big data as people use their applications and spend excessive hours on these media outlets ( DeNardis & Hackl, 2015 ). Some of the most commonly employed social media applications in the world are Facebook, Twitter, Instagram and Reddit (K. Ali, Dong, Bouguettaya, Erradi, & Hadjidj, 2017 ). Social and statistical studies have shown that these applications influence human behaviours, given the users’ length of time spent on them, which ranges from hours per week to daily use ( Statista, 2019 ).…”

“…Subsequently, turning these opinions and posts into assets is highly valuable. Discovering a Tweet or Facebook post may be possible with millions of likes and retweets, but this massive interaction with such a post does not reflect its importance or the emotions of users who participate in the post because of many factors, such as the nature of posts, including negation and irony ( Ji, Chun, Wei, Geller, & Mining, 2015 ); happiness and sadness (K. Ali, et al, 2017 ); anger ( Ji, et al, 2015 ); positive and negative ( Zarrad, Jaloud, & Alsmadi, 2014 ); concern, surprise, disgust or confusion ( Ji, Chun, & Geller, 2016 ); and the massive numbers of tweets ( Gayo-Avello, et al, 2013 ). Nevertheless, large-scale extractions of human emotions and entertainment from social media networks are essential for international public influences, business decisions and policy development ( Chung, He, & Zeng, 2015 ).…”

Ensemble Approach for Twitter Sentiment Analysis

“…Here the new quality model is presented as "Sentiment Analysis as a service" (SAaas) to evaluate multihull social information services. Spatio-temporal properties of social users are focused to identify the locations of disease outbreaks [9]. Online social network users are to be considered as social sensors that provides entrusting and required information.…”

Review helpfulness prediction: Survey

“…In this section, we will review some of these frameworks as we see in . The authors in [34] proposed a 'Sentiment Analysis as a Service' (SAaaS) framework. This framework extracted sentiments from social information services then examined and converted this data into helpful information.…”