Sentilyzer: Aspect-Oriented Sentiment Analysis of Product Reviews

Aspect-oriented sentiment analysis is used in particular on textual product reviews to identify positive or negative product characteristics. This information is beneficial not only for customers related to purchasing decisions, but also used by manufacturers to improve their products. In this short paper, we introduce Sentilyzer, a system which performs state-of-the-art aspect-oriented sentiment analysis based on automatically generated category-specific sentiment and aspect dictionaries from Amazon product reviews.

[1]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[2]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[3]  Bing Liu,et al.  Mining Opinion Features in Customer Reviews , 2004, AAAI.

[4]  Jürgen Broß,et al.  Aspect-Oriented Sentiment Analysis of Customer Reviews Using Distant Supervision Techniques , 2013 .

[5]  Flavius Frasincar,et al.  Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data , 2018, IEEE Transactions on Cybernetics.

[6]  Regina Barzilay,et al.  Automatic Aggregation by Joint Modeling of Aspects and Values , 2014, J. Artif. Intell. Res..

[7]  Arjun Mukherjee,et al.  Modeling Review Comments , 2012, ACL.

[8]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[9]  Gerhard Heyer,et al.  SentiWS - A Publicly Available German-language Resource for Sentiment Analysis , 2010, LREC.

[10]  Kim Schouten,et al.  Survey on Aspect-Level Sentiment Analysis , 2016, IEEE Transactions on Knowledge and Data Engineering.

[11]  Claire Cardie,et al.  Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis , 2008, EMNLP.

[12]  Meng Wang,et al.  Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews , 2011, ACL.

[13]  Martin Ester,et al.  ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews , 2011, SIGIR.

[14]  Themis Palpanas,et al.  Survey on mining subjective data on the web , 2011, Data Mining and Knowledge Discovery.

[15]  Shakeel Ahmad,et al.  T‐SAF: Twitter sentiment analysis framework using a hybrid classification scheme , 2018, Expert Syst. J. Knowl. Eng..

[16]  Yuji Matsumoto,et al.  Opinion Mining on the Web by Extracting Subject-Aspect-Evaluation Relations , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[17]  Mahmoud Al-Ayyoub,et al.  Enhancing Aspect-Based Sentiment Analysis of Arabic Hotels' reviews using morphological, syntactic and semantic features , 2019, Inf. Process. Manag..

[18]  Rohini K. Srihari,et al.  OpinionMiner: a novel machine learning system for web opinion mining and extraction , 2009, KDD.

[19]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).