2014
DOI: 10.1162/neco_a_00558
|View full text |Cite
|
Sign up to set email alerts
|

Feature Selection for Ordinal Text Classification

Abstract: Abstract-Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis / opinion mining community, due to the importance of automatically rating increasing amounts of product review data in digital form. As in other supervised learning tasks such as (binary or multiclass) classification, feature select… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
27
0

Year Published

2016
2016
2018
2018

Publication Types

Select...
4
3

Relationship

2
5

Authors

Journals

citations
Cited by 45 publications
(27 citation statements)
references
References 33 publications
0
27
0
Order By: Relevance
“…Indeed, ordinal regression problems can be said to be between classification and regression, presenting some similarities and differences. Some of the fields where ordinal regression is found are medical research [5]- [11], age estimation [12], brain computer interface [13], credit rating [14]- [17], econometric modelling [18], face recognition [19]- [21], facial beauty assessment [22], image classification [23], wind speed prediction [24], social sciences [25], text classification [26], and more. All these works are examples of application of specifically designed ordinal regression models, where the ordering consideration improves their performance with respect to their nominal counterparts.…”
Section: Introductionmentioning
confidence: 99%
“…Indeed, ordinal regression problems can be said to be between classification and regression, presenting some similarities and differences. Some of the fields where ordinal regression is found are medical research [5]- [11], age estimation [12], brain computer interface [13], credit rating [14]- [17], econometric modelling [18], face recognition [19]- [21], facial beauty assessment [22], image classification [23], wind speed prediction [24], social sciences [25], text classification [26], and more. All these works are examples of application of specifically designed ordinal regression models, where the ordering consideration improves their performance with respect to their nominal counterparts.…”
Section: Introductionmentioning
confidence: 99%
“…Because the average accuracy of the system is heavily influenced by ratings (such as 2 Stars and 3 Stars) that are infrequent, and as such have few training reviews. In fact, each of the five different ratings counts the same (by design -see 3) ) when computing M AE M . Table 3 provides another look at the same results, in the form of contingency tables which display, for each pair of ratings r 1 and r 2 , how many reviews whose true rating is r 1 have been rated erroneously as r 2 .…”
Section: Resultsmentioning
confidence: 99%
“…We evaluate the ability of StarTrack at correctly predicting the star-rating of a product review by a mathematical measure called macroaveraged mean absolute error (noted M AE M ); this measure, which is more fully discussed in 3) , is presented here only briefly.…”
Section: The Evaluation Measurementioning
confidence: 99%
“…However, there are aspects of ordinal classification that are receiving less attention. This is the case of feature selection methods in ordinal classification, where the number of approaches is still low [12,13]. Like some of the strategies in the feature selection literature, these techniques rely on a discretisation of the input space.…”
Section: Previous Notionsmentioning
confidence: 99%