A survey on sentiment detection of reviews
By Huifeng Tang, Songbo Tan, Xueqi Cheng. Expert systems with applications, 2009
512 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/8f1b9b97183b8aa2caa0fb6c9563b14daabe8316
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
- Fatemeh Hemmatian
- Mohammad Karim Sohrabi
- Enric Junqué de Fortuny
- David Martens
- Lin Yue
- Flavius Frasincar
- Walter Daelemans
- Ankit Agrawal
In Information Systems
- Fatemeh Hemmatian
- Mohammad Karim Sohrabi
- David Martens
- Flavius Frasincar
- Walter Daelemans
- Ankit Agrawal
- Abinash Tripathy
- Jun Gi Kim
Citations in the corpus
- Survey on Aspect-Level Sentiment Analysis (2016, background): “[12] from 2009, and while it mainly focuses on document-level machine learning approaches as well, it specifically addresses the domain of consumer reviews.”
- Evaluating and understanding text-based stock price prediction models (2014, methodology): “We chose the Support Vector Machine as the main driver for model generation since it is well established to be successful for text mining (Cohen & Hersh, 2005; Tang et al., 2009) and more specifically for stock prediction (as can be seen from Table 1).”
- A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “The important achievements of opinion mining in the commercial products are as follows (Tang et al. 2009): • Products comparison Online sellers want their customers to comment about the purchased products.”
- Sentiment analysis: A review and comparative analysis of web services (2015, unknown): “(Barbosa and Feng, 2010; Raaijmakers and Kraaij, 2008; Sarvabhotla et al., 2011; Tang et al., 2009; Esuli and Sebastiani, 2006; Montoyo et al., 2012; Maks and Vossen, 2012).”
- A survey of sentiment analysis in social media (2018, background): “Tang et al. [121] discussed related issues and main approaches to word sentiment classification, subjectivity classification, opinion extraction, and document sentiment classification.”
- Classification of sentiment reviews using n-gram machine learning approach (2016, methodology): “Methodology Classification of sentiments may be categorized into two types, i.e., binary sentiment classification and multi-class sentiment classification (Tang et al., 2009).”
- Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies (2016, methodology): “In this vein, user reviews have been used to create a classifier based on machine learning [36 – 40], and user comments on the Web have been statistically analyzed for sentiment tagging [41 – 43].”