NDLI: Using Feature Selection in Combination with Ensemble Learning Techniques to Improve Tweet Sentiment Classification Performance

Content Provider	IEEE Xplore Digital Library
Author	Prusa, J.D. Khoshgoftaar, T.M. Napolitano, A.
Copyright Year	2015
Description	Author affiliation: Florida Atlantic Univ., Boca Raton, FL, USA (Prusa, J.D.; Khoshgoftaar, T.M.; Napolitano, A.)
Abstract	Performing sentiment analysis of tweets by training a classifier is a challenging and complex task, requiring that the classifier can correctly and reliably identify the emotional polarity of a tweet. Poor data quality, due to class imbalance or mislabeled instances, may negatively impact classification performance. Ensemble learning techniques combine multiple models in an attempt to improve classification performance, especially on poor quality or imbalanced data, however, these techniques do not address the concern of high dimensionality present in tweets sentiment data and may require a prohibitive amount of resources to train on high dimensional data. This work addresses these issues by studying bagging and boosting combined with feature selection. These two techniques are denoted as Select-Bagging and Select-Boost, and seek to address both poor data quality and high dimensionality. We compare the performance of Select-Bagging and Select-Boost against feature selection alone. These techniques are tested with four base learners, two datasets and ten feature subset sizes. Our results show that Select-Boost offers the highest performance, is significantly better than using no ensemble technique, and is significantly better than Select-Bagging for most learners on both datasets. To the best of our knowledge, this is the first study to focus on the effects of using ensemble learning in combination with feature selection for the purpose of tweet sentiment classification.
Starting Page	186
Ending Page	193
File Size	312080
Page Count	8
File Format	PDF
ISSN	10823409
e-ISBN	9781509001637
DOI	10.1109/ICTAI.2015.39
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-09
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Boosting Bagging Training Feature extraction Robustness Decision trees Support vector machines SelectBagging Sentiment Analysis Tweet Mining Classification Feature Selection SelectBoost
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Using Ensemble Learners to Improve Classifier Performance on Tweet Sentiment Data

Improving Sentiment Classification Using Feature Highlighting and Feature Bagging

Enhanced Twitter Sentiment Analysis by Using Feature Selection and Combination

Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data

Lower bounds in classification for feature and algorithm selection

An empirical comparison of ensemble classification algorithms with support vector machines

A new ensemble learning with support vector machines

Combining Classification and Clustering for Tweet Sentiment Analysis

Pre-miRNA classification via combinatorial feature mining and boosting

Using Feature Selection in Combination with Ensemble Learning Techniques to Improve Tweet Sentiment Classification Performance

Similar Documents

Using Ensemble Learners to Improve Classifier Performance on Tweet Sentiment Data

Improving Sentiment Classification Using Feature Highlighting and Feature Bagging

Enhanced Twitter Sentiment Analysis by Using Feature Selection and Combination

Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data

Lower bounds in classification for feature and algorithm selection

An empirical comparison of ensemble classification algorithms with support vector machines

A new ensemble learning with support vector machines

Combining Classification and Clustering for Tweet Sentiment Analysis

Pre-miRNA classification via combinatorial feature mining and boosting

Using Feature Selection in Combination with Ensemble Learning Techniques to Improve Tweet Sentiment Classification Performance