Procedia Social and Behavioral Sciences

By Xue Zhang, Hauke Fuehres, Peter A. Gloor

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Citations in the corpus

Improving stock market prediction via heterogeneous information fusion (2017, methodology): “A method to measure the collective hope and fear on each day and analyze the correlation between these indices and the stock market indicators is proposed in [30], by which each tweet is tagged with mood words as fear, sorry, hope and so on.”
Twitter mood predicts the stock market (2010, background): “In this paper we investigate whether public sentiment, as expressed in large-scale collections of daily Twitter posts, can be used to predict the stock market.”
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks (2015, background): “There have been many works on using online users’ sentiments to predict box-ofﬁce revenues for movies (Asur and Huberman 2010), political elections (O’Connor et al. 2010; Tumasjan et al. 2010) and economic indicators (Bollen, Mao, and Zeng 2011; Zhang, Fuehres, and Gloor 2011).”
Social Media Sentiment in International Stock Returns and Trading Activity (2020, background): “Zhang, Fuehres, and Gloor (2011) and Bollen, Mao, and Zeng (2011) analyze the impact of positive and negative moods on Twitter and find that moods derived from Twitter has predictive power on U.S. stock indexes.”
Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction (2015, methodology): “A method to measure collective hope and fear on each day and analyze the correlation between these indices and the stock market indicators was proposed (Zhang et al., 2011).”
Analyzing Stock Market Movements Using Twitter Sentiment Analysis (2012, methodology): “have used corpus from livejournal blogposts in assessing the bloggers sentiment in dimensions of fear , anxiety and worry making use of Monte Carlo simulation to reflect market movements in S&P 500 index [6], [8].”
The power of prediction with social media (2013, background): “Other researchers (e.g., Gilbert and Karahalios, 2010; Bollen et al., 2011a and 2011b; or Zhang et al., 2011) have shown that public mood measured through social media (e.g., LiveJournal or Twitter) is somewhat related to stock market movements: for instance, anxiety or fear tend to predate…”
Recognizing Depression from Twitter Activity (2015, background): “Many researchers are interested in analyzing such large-scale data, and prior analyses have used those for purposes such as estimating the future movement of stock market indices and predicting the results of elections [6, 23, 38, 39, 33].”
On Using Twitter to Monitor Political Sentiment and Predict Election Results (2011, background): “who also focus on emotive concepts, in this case “hope” and “fear”, and correlate with a number of market indicators (Zhang et al., 2010).”
A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls (2014, result): “Another recent study (Wang et al., 2013) Broadly speaking, our work is also aligned to recent studies that make use of social media data to predict the stock market (Bollen et al., 2011; Zhang et al., 2011).”