Sentiment analysis using product review data
By Xing Fang, Justin Zhijun Zhan. Journal of Big Data, 2015
740 citations recorded by the source corpus.
Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis has gain much attention in recent years. In this paper, we aim to tackle the problem of sentiment polarity categorization, which is one of the fundamental problems of sentiment analysis. A general process for sentiment polarity categorization is proposed with detailed process descriptions. Data used in this study are online product reviews collected from Amazon.com. Experiments for both sentence-level categorization and review-level categorization are performed with promising outcomes. At last, we also give insight into our future work on sentiment analysis.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/5328e5dc801d1cf0f997621519f6b31d79021006
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
- Mahnaz Koupaee
- Hossein Hassani
- Maedeh Taj Mazinani
- Stephane Unger
- Richard Sicoli
- Youngseo Son
- Christina Beneki
- Truong Van Nguyen
In Information Systems
- Mahnaz Koupaee
- Hossein Hassani
- Maedeh Taj Mazinani
- Stephane Unger
- Christina Beneki
- Niranjan Balasubramanian
- Mohammad Reza Yeganegi
Citations in the corpus
- Text Mining in Big Data Analytics (2020, background): “In order to solve this problem, Fang [68] proposed a general process for the categorization of sentiment polarity.”
- Transfer learning: a friendly introduction (2022, background): “5 Polarities of sentiment analysis [44]”
- Author’s Sentiment Prediction (2020, background): “, 2011), product reviews (Dave et al., 2003; Turney, 2002; Fang and Zhan, 2015), and social media (Abbasi et al.”
- Big data analytics in supply chain management: A state-of-the-art literature review (2017, background): “BDA can help in sensing demand behaviours to increase the agility and accuracy of demand forecasting (Fang and Zhan, 2015; Salehan and Kim, 2016; Wang et al., 2014).”