Multi-classifier System for Sentiment Analysis and Opinion Mining

No abstract is available for this paper.

• Publication year

  2012

• Venue

  Encyclopedia of Social Network Analysis and Mining

• Publication date

  2012-08-26

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1007/978-1-4614-6170-8_351
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Mining the Social Web

  2013cited by this paper
• Adaptive ROC-based ensembles of HMMs applied to anomaly detection

  2012cited by this paper
• A Survey on Graphical Methods for Classification Predictive Performance Evaluation

  2011cited by this paper
• Sentiment Analysis of Twitter Data

  2011cited by this paper
• Incremental Boolean Combination of Classifiers

  2011cited by this paper
• Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment

  2010cited by this paper
• Iterative Boolean combination of classifiers in the ROC space: An application to anomaly detection with HMMs

  2010cited by this paper
• Classifying sentiment in microblogs: is brevity an advantage?

  2010cited by this paper
• Enhanced Sentiment Learning Using Twitter Hashtags and Smileys

  2010cited by this paper
• Sentiment Knowledge Discovery in Twitter Streaming Data

  2010cited by this paper
• Robust Sentiment Detection on Twitter from Biased and Noisy Data

  2010cited by this paper
• A Multi-Classifier System for Off-Line Signature Verification Based on Dissimilarity Representation

  2010cited by this paper
• Twitter as a Corpus for Sentiment Analysis and Opinion Mining

  2010cited by this paper
• An introduction to ROC analysis

  2006cited by this paper
• Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification

  2005cited by this paper
• Mining the peanut gallery: opinion extraction and semantic classification of product reviews

  2003cited by this paper
• Thumbs up? Sentiment Classification using Machine Learning Techniques

  2002cited by this paper
• Analysis of decision boundaries in linearly combined neural classifiers

  1996cited by this paper

• A Fully Bayesian Inference with Gibbs Sampling for Finite and Infinite Discrete Exponential Mixture Models

  2022cites this paper
• A Novel Approach to Estimation Precision and Recall for Star Rating Online Customers Based on Negative Hotel Reviews using Multinomial Naive Bayes over Multischeme Classifier

  2022cites this paper
• Over a Decade of Social Opinion Mining

  2020cites this paper
• Tweet Classification Framework for Detecting Events Related to Health Problems

  2017cites this paper
• Active learning in annotating micro-blogs dealing with e-reputation

  2017cites this paper
• Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms

  2015cites this paper
• Contributions on distance-based algorithms, multi-classifier construction and pairwise classification

  2015cites this paper
• User Sentiment and Opinion Analysis

  2014cites this paper
• Twitter Sentiment Analysis for the Recent Trend Extracted from the Newspaper Article

  2013cites this paper