Thumbs up? Sentiment Classification using Machine Learning Techniques

  title={Thumbs up? Sentiment Classification using Machine Learning Techniques},
  author={B. Pang and Lillian Lee and Shivakumar Vaithyanathan},
  • B. Pang, Lillian Lee, Shivakumar Vaithyanathan
  • Published in EMNLP 2002
  • Computer Science
  • We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. [...] Key Result We conclude by examining factors that make the sentiment classification problem more challenging.Expand Abstract
    7,717 Citations
    Unsupervised sentiment classification of English movie reviews using automatic selection of positive and negative sentiment items
    • John Rothfels, Julie Tibshirani June
    • 2010
    • 23
    • PDF
    CS 224 N Final Project Boost up ! Sentiment Categorization with Machine Learning Techniques
    • Andrés Cassinelli, Chih-Wei Chen June
    • 2009
    • PDF
    Sentiment Classification for Microblog by Machine Learning
    • 35
    A Comparative Study on Linguistic Feature Selection in Sentiment Polarity Classification
    • 4
    • PDF
    Sentiment analysis using Support Vector Machine
    • 90
    Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques
    • S. Rana, Archana Singh
    • Computer Science
    • 2016 2nd International Conference on Next Generation Computing Technologies (NGCT)
    • 2016
    • 36
    An Empirical Study On Sentiment Polarity Classification Of Book Reviews
    • 1
    Semi-supervised Learning for Sentiment Classification
    • PDF


    Genre Classification and Domain Transfer for Information Filtering
    • 110
    Text Categorization with Support Vector Machines: Learning with Many Relevant Features
    • 8,189
    • PDF
    Using Maximum Entropy for Text Classification
    • 962
    • Highly Influential
    • PDF
    A comparison of event models for naive bayes text classification
    • 3,572
    • PDF
    Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
    • 5,016
    • Highly Influential
    • PDF
    Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
    • 2,103
    • PDF
    Identifying Collocations for Recognizing Opinions
    • 162
    • PDF