Feature Selection for Twitter Sentiment Analysis: An Experimental Study

Riham Mansour; Mohamed Farouk Abdel Hady; Eman Hosam; Hani Amr; Ahmed Ashour

doi:10.1007/978-3-319-18117-2_7

ScienceGate Book Chapters

BOOK-CHAPTER

Feature Selection for Twitter Sentiment Analysis: An Experimental Study

Riham Mansour Mohamed Farouk Abdel Hady Eman Hosam Hani Amr Ahmed Ashour

Year: 2015 Lecture notes in computer science Pages: 92-103 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-319-18117-2_7

Get Full-Text PDF Get Analytical Report

Keywords:

SemEval Computer science Artificial intelligence Feature selection Benchmark (surveying) Sentiment analysis Ensemble learning Feature (linguistics) Selection (genetic algorithm) Pattern recognition (psychology) Task (project management) Machine learning Natural language processing

Metrics

Cited By

2.00

FWCI (Field Weighted Citation Impact)

Refs

0.86

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Sentiment Analysis and Opinion Mining

Physical Sciences → Computer Science → Artificial Intelligence

Topic Modeling

Physical Sciences → Computer Science → Artificial Intelligence

Text and Document Classification Technologies

Physical Sciences → Computer Science → Artificial Intelligence

Feature Selection for Twitter Sentiment Analysis: An Experimental Study

Metrics

Citation History

Topics

Related Documents

FEATURE SELECTION IN TWITTER SENTIMENT ANALYSIS USING ENHANCED PESOFSA

Feature Selection based Sentiment Analysis on US Airline Twitter Data

Performance Analysis of Feature Selection for Twitter Sentiment Analysis: Classification approach

Enhanced Twitter Sentiment Analysis by Using Feature Selection and Combination

Feature Selection Based Naïve Bayes Algorithm for Twitter Sentiment Analysis