The impact of social and conventional media on firm equity value: A sentiment analysis approach

By Yang Yu, Wenjing Duan, Qing Cao. Decision Support Systems, 2013

504 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a9fbb5156e6575ad60f9e0470683b4ea2023aba8

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Citations in the corpus

Author’s Sentiment Prediction (2020, background): “Sentiment analysis in news articles has been studied from various perspectives for different types of applications (Yu et al., 2013; Ahmad et al., 2007; Godbole et al., 2007; Balahur et al., 2013).”
Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis (2017, methodology): “In this study, theNBmethod (Yu et al., 2013) is used for polarity classificationwith the aimof obtaining a sentiment index for each online review.”
Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research (2016, methodology): “Yu et al. (2013) employed NLP, combined with automated sentiment analysis, based on an NB algorithm, to classify social media postings.”
Measuring social media influencer index- insights from facebook, Twitter and Instagram (2019, background): “In addition to this, existing studies explore the impact of user sentiment on firm’s equity (Yu et al., 2013).”
The effect of news and public mood on stock movements (2014, background): “[49] Sentence-level sentiment General emotion General market NA Real value NB algorithm”
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”
A survey of the applications of text mining in financial domain (2016, background): “…rate forecasting/ prediction Chatrath et al. (2014) Financial News MV Regression Mean, SD Jin et al. (2013) Financial News LR Precision/Recall Yu et al. (2013) Social Media News NB Positive/Negative, F-Measure Vu et al. (2012) Tweets DT Up/Down, Accuracy Evans and Lyons (2008) Macro News…”
A Tensor-Based Information Framework for Predicting the Stock Market (2016, methodology): “Previous studies have presented an effective method for capturing social sentiment by tracking variations in the frequency of emotion-related words in social media [Bollen et al. 2011; Yu et al. 2013; Luo et al. 2013; Li et al. 2014b].”
Tensor-Based Learning for Predicting Stock Movements (2015, unknown): “…et al. 2014a; Mittermayer and Knolmayer 2006; Schumaker and Chen 2008; 2009b; Schumaker et al. 2012; Wang, Huang, and Wang 2011; Xu and Zhang 2013; Yu, Duan, and Cao 2013) is to concatenate features of different information sources into one super feature vector, whose high dimensionalities always…”
Text mining for market prediction: A systematic review (2014, background): “Yu, Duan, and Cao (2013c) apply the Naïve Bayes (NB) algorithm to conduct sentiment analysis to examine the effect of multiple sources of social media along with the effect of conventional media and to investigate their relative importance and their interrelatedness.”
Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment (2015, background): “common approach for training and model creation in the literature (Hagenau et al., 2013; Schumaker et al., 2012; Yu et al., 2013) is some variation of the below steps:”