AspectEmo: Multi-Domain Corpus of Consumer Reviews for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a text analysis method that categorizes data by aspects and identifies the sentiment assigned to each aspect. Aspect-based sentiment analysis can be used to analyze customer opinions by associating specific sentiments with different aspects of a product or service. Most of the work in this topic is thoroughly performed for English, but many low-resource languages still lack adequate annotated data to create automatic methods for the ABSA task. In this work, we present annotation guidelines for the ABSA task for Polish and preliminary annotation results in the form of the AspectEmo corpus, containing over 1.5k consumer reviews annotated with over 63k annotations. We present an agreement analysis on the resulting annotated corpus and preliminary results using transformer-based models trained on AspectEmo.

[1]  Michał Marcińczuk.  KPWr n82 NER model (on Polish RoBERTa base) , 2020 .

[2]  Sven Rill,et al.  Evaluation of an algorithm for aspect-based opinion mining using a lexicon-based approach , 2013, WISDOM '13.

[3]  Jan Kocon,et al.  MultiEmo: Multilingual, Multilevel, Multidomain Sentiment Analysis Corpus of Consumer Reviews , 2021, ICCS.

[4]  Jan Kocoń,et al.  Context-sensitive Sentiment Propagation in WordNet , 2018, GWC.

[5]  YNU@Dravidian-CodeMix-FIRE2020: XLM-RoBERTa for Multi-language Sentiment Analysis , 2020, FIRE.

[6]  Erik Cambria,et al.  SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis , 2020, CIKM.

[7]  Idris Rabiu,et al.  Weighted aspect-based opinion mining using deep learning for recommender system , 2020, Expert Syst. Appl..

[8]  Victoria Bobicev,et al.  Emotions in Words: Developing a Multilingual WordNet-Affect , 2010, CICLing.

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[11]  Philip S. Yu,et al.  Understanding Pre-trained BERT for Aspect-based Sentiment Analysis , 2020, COLING.

[12]  Erik Cambria,et al.  ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis , 2021, Future Gener. Comput. Syst..

[13]  Lidong Bing,et al.  Exploiting BERT for End-to-End Aspect-based Sentiment Analysis , 2019, EMNLP.

[14]  Daria Puchalska,et al.  Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach , 2021, Inf. Process. Manag..

[15]  Luyao Huang,et al.  Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence , 2019, NAACL.

[16]  Idris Rabiu,et al.  Recommendation system exploiting aspect-based opinion mining with deep learning method , 2020, Inf. Sci..

[17]  Chao Yang,et al.  Aspect-based sentiment analysis with alternating coattention networks , 2019, Inf. Process. Manag..

[18]  Felipe Bravo-Marquez,et al.  A novel deterministic approach for aspect-based opinion mining in tourism products reviews , 2014, Expert Syst. Appl..

[19]  Muhammad Shahbaz,et al.  AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus , 2021, Applied Sciences.

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

[21]  Ngo Xuan Bach,et al.  Leveraging Foreign Language Labeled Data for Aspect-Based Opinion Mining , 2020, 2020 RIVF International Conference on Computing and Communication Technologies (RIVF).

[22]  Muhammad Usman,et al.  An effective model for aspect based opinion mining for social reviews , 2015, 2015 Tenth International Conference on Digital Information Management (ICDIM).

[23]  Qing Liu,et al.  Enhancing BERT Representation With Context-Aware Embedding for Aspect-Based Sentiment Analysis , 2020, IEEE Access.

[25]  Aleksander Wawer,et al.  Predicting Sentiment of Polish Language Short Texts , 2019, RANLP.

[26]  Katarzyna Wójcik.  SENTIMENT ANALYSIS OF CONSUMER OPINIONS WRITTEN IN POLISH , 2011 .

[27]  Jacobo Rouces,et al.  Aspect-Based Sentiment Analysis using BERT , 2019, NODALIDA.

[28]  Asif Ekbal,et al.  How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble [Application Notes] , 2020, IEEE Comput. Intell. Mag..

[29]  Martin Ester,et al.  The FLDA model for aspect-based opinion mining: addressing the cold start problem , 2013, WWW.

[30]  Theo Araujo,et al.  What’s the Tone? Easy Doesn’t Do It: Analyzing Performance and Agreement Between Off-the-Shelf Sentiment Analysis Tools , 2019, Communication Methods and Measures.

[31]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[32]  Jan Kocoń,et al.  Multi-Level Sentiment Analysis of PolEmo 2.0: Extended Corpus of Multi-Domain Consumer Reviews , 2019, CoNLL.

[33]  Jan Kocoń,et al.  Personal Bias in Prediction of Emotions Elicited by Textual Opinions , 2021, ACL.

[34]  George Hripcsak,et al.  Technical Brief: Agreement, the F-Measure, and Reliability in Information Retrieval , 2005, J. Am. Medical Informatics Assoc..

[35]  Erik Rolland,et al.  Modeling customer satisfaction from unstructured data using a Bayesian approach , 2016, Decis. Support Syst..

[36]  Bartosz Broda,et al.  Inforex - a web-based tool for text corpus management and semantic annotation , 2012, LREC.

[37]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[38]  Maheen Bakhtyar,et al.  Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation , 2019, IEEE Access.

[39]  Esra Akbaş,et al.  ASPECT BASED OPINION MINING ON TURKISH TWEETS , 2012 .

[40]  J AshokKumar,et al.  Aspect-based opinion ranking framework for product reviews using a Spearman's rank correlation coefficient method , 2018, Inf. Sci..

[41]  Wei Wang,et al.  Cross-domain comparison of algorithm performance in extracting aspect-based opinions from Chinese online reviews , 2016, International Journal of Machine Learning and Cybernetics.

[42]  Mingliang Chen,et al.  BuildingWord-Emotion Mapping Dictionary for Online News , 2012, SDAD@ECML/PKDD.

[43]  Martin Ester,et al.  On the design of LDA models for aspect-based opinion mining , 2012, CIKM.

[44]  Simon Fong,et al.  Fuzzy Aspect Based Opinion Classification System for Mining Tourist Reviews , 2016, Adv. Fuzzy Syst..

[45]  Alexander F. Gelbukh,et al.  Implicit Aspect Indicator Extraction for Aspect based Opinion Mining , 2014, Int. J. Comput. Linguistics Appl..

[46]  Maciej Piasecki,et al.  Recognition of Genuine Polish Suicide Notes , 2017, RANLP.

[47]  Martin Ester,et al.  Aspect-based opinion mining from product reviews , 2012, SIGIR '12.

[48]  Patrik Lambert,et al.  Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis , 2019, ACL.

[49]  Pushpak Bhattacharyya,et al.  All-in-One: Emotion, Sentiment and Intensity Prediction Using a Multi-Task Ensemble Framework , 2022, IEEE Transactions on Affective Computing.

[50]  Przemyslaw Kazienko,et al.  Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection , 2021, ACL.