Data Mining and Knowledge Engineering

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Context-Based Feature Extraction Technique – LSI vs LDA


Affiliations
1 Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
2 Department of Computer Science and Engineering, KLN Information Technology, Madurai, Tamil Nadu, India
     

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Internet has enormous amount of documents and they need to be annotated for further processing. Customer reviews or feedback on product is mostly done by using text mining or text analytics techniques. Feature extraction plays the vital role in text analytics methodology by which the most relevant features are extracted and used for text processing. This research article focuses on the use of Latent Dirichlet Allocation (LDA) as the feature extraction technique and it is compared with the prominent technique Latent Semantic Indexing (LSI).


Keywords

Text Analytics, Feature Extraction, Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), Document Categorization.
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  • Context-Based Feature Extraction Technique – LSI vs LDA

Abstract Views: 321  |  PDF Views: 1

Authors

A. M. Abirami
Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
A. Askarunisa
Department of Computer Science and Engineering, KLN Information Technology, Madurai, Tamil Nadu, India
T. S. B. Akshara
Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
G. Prasannashree
Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
K. Priyanga
Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
K. Sarika
Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India

Abstract


Internet has enormous amount of documents and they need to be annotated for further processing. Customer reviews or feedback on product is mostly done by using text mining or text analytics techniques. Feature extraction plays the vital role in text analytics methodology by which the most relevant features are extracted and used for text processing. This research article focuses on the use of Latent Dirichlet Allocation (LDA) as the feature extraction technique and it is compared with the prominent technique Latent Semantic Indexing (LSI).


Keywords


Text Analytics, Feature Extraction, Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), Document Categorization.

References