Survey on Aspect-Level Sentiment Analysis

By Kim Schouten, Flavius Frasincar. IEEE Transactions on Knowledge and Data Engineering, 2016

775 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/f90531de9e3f62c7c493a48cf33fba5b8fa661c4

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. Sven Buechel
2. Udo Hahn
3. Lin Yue
4. Mahnaz Koupaee
5. Philip S. Yu
6. Samuel Mensah
7. Ning Yang
8. Richong Zhang

In Information Systems

1. Mahnaz Koupaee
2. Philip S. Yu
3. Ning Yang
4. Udo Hahn
5. Yi Wu
6. Kai Sun
7. Yunfeng Hou
8. Sven Buechel

Citations in the corpus

• Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (2019, background): “In particular, individuals have expressed opinions on several aspects of products, services, blogs, and comments which are deemed to be influential, especially when making purchase decisions based on product reviews (Schouten and Frasincar, 2015).”
• Author’s Sentiment Prediction (2020, background): “The goal is to determine the sentiment toward specific aspect of a target such as product, movie, restaurant, etc (Pang et al., 2008; Schouten and Frasincar, 2016).”
• EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis (2017, background): “This includes the extension from only two to multiple polarity classes or even real-valued scores (Strapparava and Mihalcea, 2007), the aggregation of multiple aspects of an opinion item into a composite opinion statement for the whole item (Schouten and Frasincar, 2016), and sentiment compositional”
• Explainable recommendation with fusion of aspect information (2018, background): “They explore user opinion [32, 35, 39, 40] from reviews by means of a sentiment lexicon [8, 36, 37] or corpus [14, 28, 44] and integrate these opinion information into recommendations.”
• A survey of sentiment analysis in social media (2018, background): “Schouten et al. [111] focused on aspect-level sentiment analysis, and the goal of this paper is to find and aggregate sentiment on entities mentioned within documents or aspects of these entities.”
• A survey on sentiment analysis methods, applications, and challenges (2022, background): “Phrase level or aspect level (Schouten and Frasincar 2015) sentiment analysis performed on product reviews.”