Mining Product Features from Free-Text Customer Reviews: An SVM-Based Approach

This study examines how the Support Vector Machine (SVM) combined with natural language processing techniques can be used to identify product features from free-text customer reviews. To verify the validity of the proposed approach, 22,157 restaurant reviews are collected and 3,701 sentences are randomly selected and manually annotated. The experiment results show that the average precision and recall are both higher than those of the Maximum Entropy (ME) based approach.

[1]  Saburou Saitoh,et al.  Theory of Reproducing Kernels and Its Applications , 1988 .

[2]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[3]  Hongjun Lu,et al.  The Predicting Power of Textual Information on Financial Markets , 2005, IEEE Intell. Informatics Bull..

[4]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[5]  Liu Qun.  Chinese Lexical Analysis Using Cascaded Hidden Markov Model , 2004 .

[6]  Michael L. Littman,et al.  Measuring praise and criticism: Inference of semantic orientation from association , 2003, TOIS.

[7]  Xiaoqing Zhang,et al.  Using SOM to Mine Product Features from Free-Text Customer Reviews , 2009, 2009 Fifth International Conference on Natural Computation.

[8]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[9]  J. Anthony Gualtieri,et al.  Support vector machines for hyperspectral remote sensing classification , 1999, Other Conferences.

[10]  Chih-Jen Lin,et al.  Generalized Bradley-Terry Models and Multi-Class Probability Estimates , 2006, J. Mach. Learn. Res..

[11]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[12]  Sasha Blair-Goldensohn,et al.  Building a Sentiment Summarizer for Local Service Reviews , 2008 .

[13]  Wu Meng,et al.  Application of Support Vector Machines in Financial Time Series Forecasting , 2007 .

[14]  Hsuan-Tien Lin.  A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .