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To use or not to use: Feature selection for sentiment analysis of highly imbalanced data†

Published online by Cambridge University Press:  07 August 2017

SANDRA KÜBLER ,
CAN LIU  and
ZEESHAN ALI SAYYED
SANDRA KÜBLER
Affiliation:
Department of Linguistics, Indiana University, Bloomington, IN 47405, USA e-mail: skuebler@indiana.edu
CAN LIU
Affiliation:
Department of Computer Science, Indiana University, Bloomington, IN 47405, USA e-mails: liucan@indiana.edu, zasayyed@indiana.edu
ZEESHAN ALI SAYYED
Affiliation:
Department of Computer Science, Indiana University, Bloomington, IN 47405, USA e-mails: liucan@indiana.edu, zasayyed@indiana.edu
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Abstract

We investigate feature selection methods for machine learning approaches in sentiment analysis. More specifically, we use data from the cooking platform Epicurious and attempt to predict ratings for recipes based on user reviews. In machine learning approaches to such tasks, it is a common approach to use word or part-of-speech n-grams. This results in a large set of features, out of which only a small subset may be good indicators for the sentiment. One of the questions we investigate concerns the extension of feature selection methods from a binary classification setting to a multi-class problem. We show that an inherently multi-class approach, multi-class information gain, outperforms ensembles of binary methods. We also investigate how to mitigate the effects of extreme skewing in our data set by making our features more robust and by using review and recipe sampling. We show that over-sampling is the best method for boosting performance on the minority classes, but it also results in a severe drop in overall accuracy of at least 6 per cent points.

Type
Articles
Information
Natural Language Engineering , Volume 24 , Issue 1 , January 2018 , pp. 3 - 37
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

†

This work is based on research supported by the U.S. Office of Naval Research (ONR) via grant #N00014-10-1-0140.

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