Improving Sentiment Analysis of Moroccan Tweets Using Ensemble Learning

Communications in Computer and Information Science - Big Data, Cloud and Applications ◽

10.1007/978-3-319-96292-4_8 ◽

2018 ◽

pp. 91-104 ◽

Cited By ~ 4

Author(s):

Ahmed Oussous ◽

Ayoub Ait Lahcen ◽

Samir Belfkih

Keyword(s):

Sentiment Analysis ◽

Ensemble Learning

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Simple Approaches of Sentiment Analysis via Ensemble Learning

Lecture Notes in Electrical Engineering - Information Science and Applications ◽

10.1007/978-3-662-46578-3_74 ◽

2015 ◽

pp. 631-639 ◽

Cited By ~ 16

Author(s):

Tawunrat Chalothom ◽

Jeremy Ellman

Keyword(s):

Sentiment Analysis ◽

Ensemble Learning

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Social Sentiment Analysis Using Classifiers and Ensemble Learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/1237/2/022193 ◽

2019 ◽

Vol 1237 ◽

pp. 022193

Author(s):

Linxiang Zhang

Keyword(s):

Sentiment Analysis ◽

Ensemble Learning

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Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

Algorithms ◽

10.3390/a13040083 ◽

2020 ◽

Vol 13 (4) ◽

pp. 83 ◽

Cited By ~ 5

Author(s):

Giannis Haralabopoulos ◽

Ioannis Anagnostopoulos ◽

Derek McAuley

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Ensemble Learning ◽

Affective Computing ◽

Binary Classification ◽

User Generated Content ◽

Emotional Information ◽

Multiple Classifiers ◽

Different Levels

Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % .

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