Journal ArticleDOI
Document-level sentiment classification: An empirical comparison between SVM and ANN
TLDR
An empirical comparison between SVM and ANN regarding document-level sentiment analysis is presented and it is indicated that ANN produce superior or at least comparable results to SVM's, even on the context of unbalanced data.Abstract:
Document-level sentiment classification aims to automate the task of classifying a textual review, which is given on a single topic, as expressing a positive or negative sentiment. In general, supervised methods consist of two stages: (i) extraction/selection of informative features and (ii) classification of reviews by using learning models like Support Vector Machines (SVM) and Nai@?ve Bayes (NB). SVM have been extensively and successfully used as a sentiment learning approach while Artificial Neural Networks (ANN) have rarely been considered in comparative studies in the sentiment analysis literature. This paper presents an empirical comparison between SVM and ANN regarding document-level sentiment analysis. We discuss requirements, resulting models and contexts in which both approaches achieve better levels of classification accuracy. We adopt a standard evaluation context with popular supervised methods for feature selection and weighting in a traditional bag-of-words model. Except for some unbalanced data contexts, our experiments indicated that ANN produce superior or at least comparable results to SVM's. Specially on the benchmark dataset of Movies reviews, ANN outperformed SVM by a statistically significant difference, even on the context of unbalanced data. Our results have also confirmed some potential limitations of both models, which have been rarely discussed in the sentiment classification literature, like the computational cost of SVM at the running time and ANN at the training time.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
Sentiment analysis algorithms and applications: A survey
TL;DR: This survey paper tackles a comprehensive overview of the last update in this field of sentiment analysis with sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.
Journal ArticleDOI
A survey on opinion mining and sentiment analysis
Kumar Satish Ravi,Vadlamani Ravi +1 more
TL;DR: A rigorous survey on sentiment analysis is presented, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis.
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Deep learning for sentiment analysis: A survey
Lei Zhang,Shuai Wang,Bing Liu +2 more
TL;DR: Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results as mentioned in this paper, which is also popularly used in sentiment analysis in recent years.
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Sentiment Analysis: Mining Opinions, Sentiments, and Emotions
TL;DR: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes as discussed by the authors, which offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis.
References
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