The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
ARSA: a sentiment-aware model for predicting sales performance using blogs
Abstract
Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.
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Cites
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Cited by
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
“Over the past 5 years significant progress has been made in sentiment tracking techniques that extract indicators of public mood directly from social media content such as blog content [10], [12], [15], [17] and in particular large-scale Twitter feeds [22].”
BackgroundMethodTowards detecting influenza epidemics by analyzing Twitter messages
Culotta, A. (2010) · Proceedings of the First Workshop on Social Media Analytics · doi:10.1145/1964858.1964874 · Cited by 608
A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing
Liu, X., Singh, P. V., & Srinivasan, K. (2016) · Marketing Science · doi:10.1287/mksc.2015.0972 · Cited by 184
ChronoSeeker: search engine for future and past events
Kawai, H., Jatowt, A., Tanaka, K., Kunieda, K., & Yamada, K. (2010) · Proceedings of the 4th International Conference on Uniquitous Information Management and Communication · doi:10.1145/2108616.2108647 · Cited by 22
Supporting analysis of future-related information in news archives and the web
Jatowt, A., Kanazawa, K., Oyama, S., & Tanaka, K. (2009) · Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries · doi:10.1145/1555400.1555420 · Cited by 21
Metadata
- Authors
- Liu, Y., Huang, X., An, A., & Yu, X.
- Venue
- Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2007-07-23
- Pages
- 607-614
- OpenAlex
- W1983286042
- Cited by
- 306
- References
- 22
- FWCI
- 6.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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