This paper presents a regression model with
autocorrelated errors in which the inputs are social moods obtained by
analyzing the adjectives in Twitter posts using a document topic
model, where document topics are extracted using LDA. The
regression model predicts Dow Jones Industrial Average (DJIA) more
precisely than autoregressive moving-average models.
 Bollen, J., H. Mao, and X.-J. Zeng (2011). Twitter mood predicts the
stock market, Journal of Computational Science, 2, 1, pp.1-8.
 Lorr, M., D. M. McNair, and J. Heuchert (2011). Poms bi-polar manual
supplement. Multi-Health System Inc.
 Ohomura, M., K. Kakusho, T. Okadome (2014). Social mood extraction
from Twitter posts with document topic model, Proceedings of the Fifth
International Conference on Information Science and Applications
(ICISA2014), 357-360, Soul, May 2014.
 Golder, S. A. and M. W. Macy (2011). Diurnal and seasonal mood vary
with work, sleep, and daylength across diverse cultures. Science, 333,
6051, pp. 1878-1881.
 Lee, C.-H., C.-H. Wu, and T.-F. Chien (2011). BursT: A dynamic term
weighting scheme for mining microblogging messages, Proceedings of
the 8th international Conference on Advances in Neural Networks -
Volume Part III, pp. 548-557.
 Blei, D. M., A. Y. Ng, and M. I. Jordan (2003). Latent Dirichlet
allocation, Journal of Machine Learning Research, 3, pp. 993-1022.
 Blei, D. M. (2012). Probabilistic topic model, Communications of the
Association for Computing Machinery, 55, 4, pp. 77-84.
 Fellbaum, C. and G. Miller (1988). WordNet: An Electronic Lexical
Database (Language, Speech, and Communication). MIT Press.
 Shumway, R. H. and D. S. Stoffer (2011). Time Series Analysis and Its
Applications (Third Edition). Springer, New York.