Document-level sentiment classification: An empirical comparison between SVM and ANN

By Rodrigo Moraes, João Francisco Valiati, Wilson P. Gavião Neto. Expert systems with applications, 2013

749 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/48c5818ff11b865a471c0cd3b426b5eeb47dcfb2

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In Artificial Intelligence

1. B. Liu
2. Shuai Wang
3. Bing Liu
4. Lei Zhang
5. Vadlamani Ravi
6. Kumar Ravi
7. Aytuğ Onan
8. Fatemeh Hemmatian

Citations in the corpus

• Interpreting Neural Networks to Improve Politeness Comprehension (2016, methodology): “work has also applied neural network based models to sentiment analysis tasks (Chen et al., 2011; Socher et al., 2013; Moraes et al., 2013; Dong et al., 2014; dos Santos and Gatti, 2014; Kalchbrenner et al., 2014).”
• Sentiment analysis: A review and comparative analysis of web services (2015, methodology): “Regarding the way of assessing this work, and following recent works [101,58], the accuracy, recall, precision and the mean square error have been selected as measures to assess the different capabilities of each tool.”
• Deep learning for sentiment analysis: A survey (2018, result): “Moraes et al. 34 made an empirical comparison between Support Vector Machines (SVM) and Artificial Neural Networks (ANN) for document level sentiment classification, which demonstrated that ANN produced competitive results to SVM’s in most cases.”
• A survey on sentiment analysis methods, applications, and challenges (2022, background): “In work of Moraes et al. (2013a) presented a comparative empirical analysis between SVM and ANN in document-level sentiment analysis.”
• Sentiment analysis algorithms and applications: A survey (2014, methodology): “There is an empirical comparison between SVM and Artifi-cial neural networks ANNs presented by Moraes and Valiati [53] regarding document-level sentiment analysis.”
• A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”
• Author’s Sentiment Prediction (2020, background): “Many works have studied document-level sentiment analysis (Pang et al., 2002; Moraes et al., 2013; Lin et al., 2006; Tang et al., 2015).”
• A survey on classification techniques for opinion mining and sentiment analysis (2017, methodology): “The classification on the document level (Moraes et al. 2013) and sentence level (Marcheggiani et al. 2014) does not provide these kinds of information and we need to perform opinion mining in aspect level (Xia et al. 2015) to achieve these details.”