Recognizing emotions in text using ensemble of classifiers

Perikos, Isidoros; Hatzilygeroudis, Ioannis

doi:10.1016/j.engappai.2016.01.012

Cited by 153 publications

(63 citation statements)

References 24 publications

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“…Ensemble of classifiers have been used in many different fields (Perikos and Hatzilygeroudis, 2016;Yang et al, 2015). The accuracy obtained by ensembles is usually higher than that of the individual classifiers that comprise them.…”

Section: Ensemblementioning

confidence: 99%

An ensemble of ordered logistic regression and random forest for child garment size matching

Pierola

Epifanio

Alemany

2016

Computers & Industrial Engineering

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Size fitting is a significant problem for online garment shops. The return rates due to size misfit are very high. We propose an ensemble (with an original and novel definition of the weights) of ordered logistic regression and random forest (RF) for solving the size matching problem, where ordinal data should be classified. These two classifiers are good candidates for combined use due to their complementary characteristics. A multivariate response (an ordered factor and a numeric value assessing the fit) was considered with a conditional random forest. A fit assessment study was carried out with 113 children. They were measured using a 3D body scanner to obtain their anthropometric measurements. Children tested different garments of different sizes, and their fit was assessed by an expert. Promising results have been achieved with our methodology. Two new measures have been introduced based on RF with multivariate responses to gain a better understanding of the data. One of them is an intervention in prediction measure defined locally and globally. It is shown that it is a good alternative to variable importance measures and it can be used for new observations and with multivariate responses. The other proposed tool informs us about the typicality of a case and allows us to determine archetypical observations in each class.Keywords: Multivariate conditional random forest; Proportional odds logistic regression; Supervised learning; Ordinal classification; Childrenswear garment fitting; Variable importance ACKNOWLEDGEMENTS This work has been partially supported by Grants DPI2013-47279-C2-1-R and DPI2013-47279-C2-2-R.An ensemble of ordered logistic regression and random forest for child garment size matchingSize fitting is a significant problem for online garment shops. The return rates due to size misfit are very high. We propose an ensemble (with an original and novel definition of the weights) of ordered logistic regression and random forest (RF) for solving the size matching problem, where ordinal data should be classified. These two classifiers are good candidates for combined use due to their complementary characteristics. A multivariate response (an ordered factor and a numeric value assessing the fit) was considered with a conditional random forest. A fit assessment study was carried out with 113 children. They were measured using a 3D body scanner to obtain their anthropometric measurements. Children tested different garments of different sizes, and their fit was assessed by an expert. Promising results have been achieved with our methodology. Two new measures have been introduced based on RF with multivariate responses to gain a better understanding of the data. One of them is an intervention in prediction measure defined locally and globally. It is shown that it is a good alternative to variable importance measures and it can be used for new observations and with multivariate responses. The other proposed tool informs us about the typicality of a case and allows us to determine archetypical obs...

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Section: Ensemblementioning

confidence: 99%

An ensemble of ordered logistic regression and random forest for child garment size matching

Pierola

Epifanio

Alemany

2016

Computers & Industrial Engineering

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“…In this section, we show the superiority of our possibilistic approach (PA) compared to an existing approach in the work of Perikos and Hatzilygeroudis in terms of accuracy, precision, sensitivity, and specificity. This latter approach introduces a sentiment analysis system for automatic recognition of emotions in text.…”

Section: Experimental Studymentioning

confidence: 99%

A possibilistic framework for the detection of terrorism‐related Twitter communities in social media

Moussaoui

Zaghdoud

Akaichi

2018

Concurrency and Computation

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Summary Since the appearance of social networks, there was a historic increase of data. Unfortunately, terrorists are taking advantage of the easiness of accessing social networks and they have set up profiles to recruit, radicalize, and raise funds. Most of these profiles have pages that exist as well as new recruits to join the terrorist groups, see, and share information. Therefore, there is a potential need for detecting terrorist communities in social networks in order to search for key hints in posts that appear to promote the militants' cause. In order to remedy this problem, we first use a possibilistic‐clustering algorithm that allows more flexibility when assigning a social network profile to clusters (non‐terrorist, terrorist‐sympathizer, terrorist). Then, we introduce a new possibilistic flexible graph mining method to discover similar subgraphs by applying possibilistic similarity rather than using hard structural exact similarity. We experimentally show the efficiency of our possibilistic approach through a detailed process of tweets extract, semantic processing, and classification of the community detection.

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“…Cotelo et al [22] combined the structural information and textual concepts of tweets with direct combination, stacked generalization and Multiple Pipeline Stacked Generalization methods. Isidoros Perikos and Ioannis Hatzilygeroudis [23] presented a sentiment analysis system for automatic recognition of emotions in text, using an ensemble model based on a Naï ve Bayes, a Maximum Entropy learner and a knowledge-based tool. Corrê a Jr. et al [24] …”

Section: Related Workmentioning

confidence: 99%

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Sevim¹,

Omurca²,

Ekinci³

2018

IJITCS

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Abstract-With the widespread usage of social media in our daily lives, user reviews emerged as an impactful factor for numerous fields including understanding consumer attitudes, determining political tendency, revealing strengths or weaknesses of many different organizations. Today, people are chatting with their friends, carrying out social relations, shopping and following many current events through the social media. However social media limits the size of user messages. The users generally express their opinions by using emoticons, abbreviations, slangs, and symbols instead of words. This situation makes the sentiment classification of social media texts more complex. In this paper a sentiment classification model for Twitter messages is proposed to overcome this difficulty. In the proposed model first the short messages are expanded with BabelNet which is a concept network. Then the expanded and the original form of the messages are included in an ensemble learning model. Consequently we compared our ensemble model with traditional classification algorithms and observed that the F-measure value is increased.

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Recognizing emotions in text using ensemble of classifiers

Cited by 153 publications

References 24 publications

An ensemble of ordered logistic regression and random forest for child garment size matching

An ensemble of ordered logistic regression and random forest for child garment size matching

A possibilistic framework for the detection of terrorism‐related Twitter communities in social media

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

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