Paper Info
Title
Performance analysis of Ensemble methods on Twitter sentiment analysis using NLP techniques
Abstract
Mining opinions and analyzing sentiments from social network data help in various fields such as even prediction, analyzing overall mood of public on a particular social issue and so on. This paper involves analyzing the mood of the society on a particular news from Twitter posts. The key idea of the paper is to increase the accuracy of classification by including Natural Language Processing Techniques (NLP) especially semantics and Word Sense Disambiguation. The mined text information is subjected to Ensemble classification to analyze the sentiment. Ensemble classification involves combining the effect of various independent classifiers on a particular classification problem. Experiments conducted demonstrate that ensemble classifier outperforms traditional machine learning classifiers by 3-5%.
Year
DOI
Venue
2015
10.1109/ICOSC.2015.7050801
Semantic Computing
Keywords
Field
DocType
learning (artificial intelligence),natural language processing,pattern classification,social networking (online),text analysis,nlp techniques,twitter posts,twitter sentiment analysis,classification problem,ensemble classification,ensemble classifier,ensemble methods,machine learning classifiers,natural language processing techniques,performance analysis,social network data,text information,word sense disambiguation,sentiment analysis,social network analysis,classification algorithms,algorithm design and analysis,prediction algorithms,semantics
Social network,Algorithm design,Sentiment analysis,Computer science,Artificial intelligence,Natural language processing,Classifier (linguistics),Statistical classification,Ensemble learning,Word-sense disambiguation,Machine learning,Semantics
Conference
ISSN
Citations 
PageRank 
2325-6516
4
0.45
References 
Authors
6
2
Name
Order
Citations
PageRank
Monisha Kanakaraj140.45
Ram Mohana Reddy Guddeti2488.76