Feature selection through visualisation for the classification of online reviews

The purpose of this work is to prove that the visualization is at least as powerful as the best automatic feature selection algorithms. This is achieved by applying our visualization technique to the online review classification into fake and genuine reviews. Our technique uses radial chart and color overlaps to explore the best feature selection through visualization for classification. Every review is treated as a radial translucent red or blue membrane with its dimensions determining the shape of the membrane. This work also shows how the dimension ordering and combination is relevant in the feature selection process. In brief, the whole idea is about giving a structure to each text review based on certain attributes, comparing how different or how similar the structure of the different or same categories are and highlighting the key features that contribute to the classification the most. Colors and saturations aid in the feature selection process. Our visualization technique helps the user get insights into the high dimensional data by providing means to eliminate the worst features right away, pick some best features without statistical aids, understand the behavior of the dimensions in different combinations.

Discipline thesis:degree_discipline

Computer & Information Science

Year dc:date.issued

2017

Author dc:creator

• Koka, Keerthika

Advisor dc:contributor.advisor

• Fang, Shiaofen

• Text Visual analytics
• Data visualisation
• Online reviews classification
• Multi-dimensional data visualisation
• Visual feature selection

Language dc:language.iso

en_US

DOI

10.7912/C2XM1D

Identifier URI

http://dx.doi.org/10.7912/C2/2346

OAI identifier oai:identifier

oai:scholarworks.indianapolis.iu.edu:1805/12483