Mining comparative opinions from customer reviews for Competitive Intelligence

By Kaiquan Xu, S. Liao, Jiexun Li, Yuxia Song. Decision Support Systems, 2011

358 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f785dbd8ce064c44cd20e32633f611a2be8f9de0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Vadlamani Ravi
2. Kumar Ravi
3. Z. Fan
4. Jianwei Bi
5. Yang Liu
6. A. Hassan
7. Shiliang Sun
8. Chen Luo

In Statistical and Nonlinear Physics

1. A. Hassan
2. Kumar Ravi
3. Vadlamani Ravi
4. Shiliang Sun
5. Walaa Medhat
6. H. Korashy

Citations in the corpus

• A review of natural language processing techniques for opinion mining systems (2017, methodology): “The information contained in comparative reviews can help enterprises discover potential risks and further improve products or marketing strategies [4].”
• Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory (2017, methodology): “On the other hand, the lexicon-based sentiment analysis techniques are further divided into two subclasses, i.e., (1) dictionary-based sentiment analysis techniques [38–40] , and (2) corpus-based sentiment analysis techniques [25, 41] .”
• 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”
• Sentiment analysis algorithms and applications: A survey (2014, methodology): “Table 1 contains the articles reference [4–7] and [12–61].”
• Sentiment analysis using deep learning architectures: a review (2019, background): “…Tripathy (2017) Amazon health product reviews Vanilla RNN LSTM GRU Bi-Vanilla RNN Bi-LSTM Bi-GRU 57.30 78.10 83.90 58.00 79.20 81.10 Amazon: 8000 Xu et al. (2011) Amazon mobile phone reviews Multi-class SVM CRF without interdependencies CRF with interdependencies 61.38 60.04 66.17 – Rain (2013)…”