Sentiment in Twitter events

By Mike Thelwall, Kevan Buckley, Georgios Paltoglou. J. Assoc. Inf. Sci. Technol., 2011

879 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/86f79872ac3e82bd6969a43434f2d7d16403ceb1

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Citations in the corpus

Predicting political preference of Twitter users (2013, methodology): “SentiStrength has been successfully used in estimating positive and negative sentiment in short texts such as twitter posts (Thelwall et al. 2011) and Yahoo answers (Kucuktunc et al.”
How Translation Alters Sentiment (2016, background): “…for tweets include those by Go, Bhayani, and Huang (2009), Pak and Paroubek (2010), Agarwal, Xie, Vovsha, Rambow, and Passonneau (2011), Thelwall, Buckley, and Paltoglou (2011), Brody and Diakopoulos (2011), Aisopos, Papadakis, Tserpes, and Varvarigou (2012), Bakliwal, Arora,…”
More than words: Social networks' text mining for consumer brand sentiments (2013, methodology): “Following Thelwall et al. (2011), only tweets in English was chosen in order to remove complications that might arise with analyzing multilingual tweets.”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “Document level 73 [13], [18], [22], [32], [33], [36], [40], [43], [45], [48], [50], [51], [53], [54], [61], [64], [66], [77], [81], [80], [85], [88], [90], [91], [94], [96], [101], [111], [117], [121], [123], [130], [131], [132], [148], [155], [156], [157], [158], [167], [168], [169], [175], [176], ”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, methodology): “12 Thomas et al. (2014) applied supervised machine learning techniques on the ISEAR dataset for 7-way emotion classification.”
Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior (2013, methodology): “It has been proven useful to classify emotions in short informal messages from Myspace and twitter [69].”
Measuring Emotional Contagion in Social Media (2015, methodology): “In this work, we use SentiStrength [44, 43, 41] to annotate the tweets with positive and negative sentiment scores.”
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments (2010, background): “…( twitter.com ) is one particularly fruitful source of user-created content, and a ﬂurry of recent research has aimed to understand and exploit these data (Ritter et al., 2010; Shar-iﬁ et al., 2010; Barbosa and Feng, 2010; Asur and Huberman, 2010; O’Connor et al., 2010a; Thelwall et al., 2011).”
Sentiment strength detection for the social web (2012, background): “As discussed above, the exploitation of topic is undesirable for some applications, particularly if the focus is on changes in sentiment (Thelwall et al., 2011) or identifying clusters of sentiment (Chmiel et al.”
Challenges in Sentiment Analysis (2017, neutral): “…analysis systems developed speciﬁcally for tweets include those by Pak and Paroubek (2010), Agarwal, Xie, Vovsha, Rambow, and Passonneau (2011), Thelwall, Buckley, and Paltoglou (2011), Brody and Diakopoulos (2011), Aisopos, Papadakis, Tserpes, and Varvarigou (2012), Bakliwal, Arora,…”
Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp (2017, neutral): “Research has shown that on Twitter, in part due to these characteristics, content is mainly negatively valenced even when it concerns positive events (Naveed et al., 2011; Thelwall et al., 2011).”
Sentiment Analysis of Short Informal Texts (2014, background): “…analysis systems developed speciﬁcally for tweets include those by Pak and Paroubek (2010), Agarwal, Xie, Vovsha, Rambow, and Passonneau (2011), Thelwall, Buckley, and Paltoglou (2011), Brody and Diakopoulos (2011), Aisopos, Papadakis, Tserpes, and Varvarigou (2012), Bakliwal, Arora,…”