Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches

By Pimwadee Chaovalit, Lina Zhou. Proceedings of the Annual Hawaii International Conference on System Sciences, 2005

441 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/28633b18c2b3bad80b1edb552146cc200b90f0e7

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In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Survey on mining subjective data on the web (2011, methodology): “Extending on this work, Chaovalit and Zhou (2005) used Google's search engine to determine the co-occurrence of words, increasing the precision.”
An empirical study of sentiment analysis for chinese documents (2008, background): “Chaovalit and Zhou (2005) compared machine learning based methods and orientation based methods.”
Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization (2006, methodology): “Sentiment analysis of text documents has received considerable attention recently (Shanahan et al., 2005; Turney, 2002; Dave et al., 2003; Hu and Liu, 2004; Chaovalit and Zhou, 2005).”
A survey on sentiment detection of reviews (2009, neutral): “Koppel, & Avneri, 1998; Kessler, Nunberg, & SchAutze, 1997; Spertus, 1997), but only in the early 2000s did it become a major subfield of the information management discipline ( Chaovalit & Zhou, 2005; Dimitrova, Finn, Kushmerick, & Smyth, 2002; Durbin, Neal Richter, & Warner, 2003; Efron, 2004; Ga”
Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier (2016, background): “27 % when applied on the test data set [4].”
Sentiment Analysis on Social Media (2012, methodology): “Chaovalit and Zhou [6] evaluated common implementations for both techniques on movie reviews and concluded that Supervised techniques perform with about 85% accuracy, whereas Unsupervised methods perform about 77%.”
Computational approaches for mining user's opinions on the Web 2.0 (2014, methodology): “Due to the number of different techniques, several researchers experimented with different algorithms and drew comparisons between them: (Chaovalit & Zhou, 2005; Cui, Mittal, & Datar, 2006; Moghaddam & Ester, 2012).”
Aspect-based sentiment analysis of movie reviews on discussion boards (2010, background): “Sentiment analysis of movie reviews is considered more challenging than sentiment analysis of other types of reviews, such as product reviews [4, 5].”
Opinion Mining and Sentiment Analysis (2008, methodology): “A comparison of supervised and unsupervised methods can be found in Chaovalit and Zhou [55].”
Using text mining and sentiment analysis for online forums hotspot detection and forecast (2010, methodology): “As canbe seen, our integrated approach differs from existing sentiment calculationwork, which is either based onmachine learning [3,33] or semantic orientation [1,26–28,33,35].”
Movie review mining and summarization (2006, background): “In [2], the authors compared two kinds of approaches based on machine learning and semantic orientation systematically.”