Sentiment analysis in Facebook and its application to e-learning

By Alvaro Ortigosa, José M. Martín, Rosa M. Carro. Computers in Human Behavior, 2014

613 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/ffe00353d72de7cce64bcb797083b0ed39597ce6

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Citations in the corpus

A survey of sentiment analysis in social media (2018, methodology): “[80] presented a new method for sentiment analysis in Facebook.”
Sentiment Analysis: An Overview from Linguistics (2016, positive): “…applications have involved forms of blogging and microblogging, such as Twitter or Facebook messages (Kiritchenko et al 2014, Mohammad et al 2013, Ortigosa et al 2014, Thelwall et al to appear, Vilares et al 2015), including the Hedonometer project, an attempt to measure happiness in Twitter…”
Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, background): “Document-level polarity classiﬁcation is an essential requirement for studies such as social and psychological studies in social networks [Ortigosa et al. 2014; Gao et al. 2015a], consumer satisfaction [Kang and Park 2014], and analyzing patients in medical settings [Denecke and Deng 2015].”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”
Depression detection from social network data using machine learning techniques (2018, background): “Ortigosa et al. [24] have exhibited a new strategy for sentiment examine in Facebook that suggests that starting from messages composed by users, as to extract data about the users’ assessment extremity (positive, unbiased or negative), as transmitted in the messages they write; and to show the…”
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, methodology): “[64] suggested a sentiment classification and change detection model using a lexicon and machine learning approach on the Facebook comments dataset.”