Using text mining and sentiment analysis for online forums hotspot detection and forecast

By Nan Li, Desheng Dash Wu. Decision Support Systems, 2010

509 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1af8607b00516de0bb4fa15c11ac4013658f5a92

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In Artificial Intelligence

In Statistical and Nonlinear Physics

Citations in the corpus

Predicting the “helpfulness” of online consumer reviews (2017, background): “The sentiments of the review have a direct impact on sales as stated by several researchers (Li & Wu, 2010; Liu, Yu, An, & Huang, 2013; Schumaker, Zhang, Huang, & Chen, 2012; Siering & Muntermann, 2013) revealed a very unique property of reviews, indicating that reviews with information related to…”
The impact of social and conventional media on firm equity value: A sentiment analysis approach (2013, background): “Sentiment analysis is the computational detection and study of opinions, sentiments, emotions, and subjectivities in text [22,23,30].”
Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020, background): “Designing effective algorithms that are capable of automatically performing sentiment analysis and opinion mining is a challenging and important task in the field of natural language processing (Pang and Lee, 2008; Liu, 2010; Ortigosa et al., 2014; Smailović et al., 2013; Li and Wu, 2010).”
Operational Transparency on Crowdfunding Platforms: Effect on Donations for Emergency Response (2019, background): “For example, researchers have analyzed user-generated content to measure the ﬁnancial health of ﬁrms and markets (Antweiler and Frank 2004, Li 2010, Loughran and Mcdonald 2011, Schumaker and Chen 2009, Tetlock et al. 2008), to forecast customer preferences and choice (Cao et al. 2011, Decker and…”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “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”
Exploring online customer engagement with hospitality products and its relationship with involvement, emotional states, experience and brand advocacy (2019, methodology): “Such analysis is performed based on published online reviews and comments (Költringer & Dickinger, 2015) and can be made through computational detection and study of sentiments in the text (Li & Wu, 2010).”
More than words: Social networks' text mining for consumer brand sentiments (2013, background): “, Lim, 2012; Papacharissi & Oliveira, 2012). These studies found that Twitter was used by protesters as an alternative to the blocked access to the Internet. The continuous stream of events provided by Twitter users was also found to be an accurate predictor of the crisis outcome. Park, Lim, Sams, N”