TISA: Topic Independence Scoring Algorithm

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https://link.springer.com/chapter/10.1007/978-3-642-39712-7_43

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https://doi.org/10.1007/978-3-642-39712-7_43
 http://hdl.handle.net/11603/30407

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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https://orcid.org/0000-0002-6593-1792

Date

2013-07-13

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book chapters
conference papers and proceedings
postprints
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Citation of Original Publication

Martineau, J.C., Cheng, D., Finin, T. (2013). TISA: Topic Independence Scoring Algorithm. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science, vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_43

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Abstract

Textual analysis using machine learning is in high demand for a wide range of applications including recommender systems, business intelligence tools, and electronic personal assistants. Some of these applications need to operate over a wide and unpredictable array of topic areas, but current in-domain, domain adaptation, and multi-domain approaches cannot adequately support this need, due to their low accuracy on topic areas that they are not trained for, slow adaptation speed, or high implementation and maintenance costs. To create a true domain-independent solution, we introduce the Topic Independence Scoring Algorithm (TISA) and demonstrate how to build a domain-independent bag-of-words model for sentiment analysis. This model is the best preforming sentiment model published on the popular 25 category Amazon product reviews dataset. The model is on average 89.6% accurate as measured on 20 held-out test topic areas. This compares very favorably with the 82.28% average accuracy of the 20 baseline in-domain models. Moreover, the TISA model is highly uniformly accurate, with a variance of 5 percentage points, which provides strong assurance that the model will be just as accurate on new topic areas. Consequently, TISAs models are truly domain independent. In other words, they require no changes or human intervention to accurately classify documents in never before seen topic areas.