ABSTRACT
Nowadays, there are more and more social networks and Web platforms that give their users the opportunity to share their opinions and tastes on items of different types. This inevitably led to a growth of data relating to the subjective sphere of each individual. This information is extremely useful for several purposes, such as providing personalized recommendation services or understanding opinions conveyed through text. Sentiment Analysis provides helpful methods to analyze these textual opinions (e.g. reviews) from a global point of view. In case we want a more detailed representation of the opinion represented in a text, Aspect-based Sentiment Analysis identifies a valuable option thanks to its fine-grained level of text analysis.
In this paper, we have designed a processing pipeline aimed to extracting domain-related aspects from text by means of an unsupervised approach. We formally define Aspect Terms and Aspect Categories as well as Aspect-based Sentiment Embedding, an approach of representing documents by computing aggregated sentiment scores for each aspect. We perform experimental evaluations on the Spotify dataset to prove the utility of our technique in predicting elements strictly related to emotions and feelings. Our results show improvements on the regression task for sentiment-related features compared to the classical semantic-based representations.
- Pedro Álvarez, A. Guiu, José Ramón Beltrán, J. García de Quirós, and Sandra Baldassarri. 2019. DJ-Running: An Emotion-based System for Recommending Spotify Songs to Runners. In Proceedings of the 7th International Conference on Sport Sciences Research and Technology Support, icSPORTS 2019, Vienna, Austria, September 20--21, 2019, João Vilas-Boas, Pedro Pezarat Correia, and Jan Cabri (Eds.). ScitePress, 55--63.Google Scholar
- Mohammad Ehsan Basiri, Shahla Nemati, Moloud Abdar, Erik Cambria, and U. Rajendra Acharya. 2021. ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis. Future Gener. Comput. Syst. 115 (2021), 279--294.Google ScholarCross Ref
- Ranjan Kumar Behera, Monalisa Jena, Santanu Kumar Rath, and Sanjay Misra. 2021. Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Inf. Process. Manag. 58, 1 (2021), 102435.Google ScholarCross Ref
- Ganpat Singh Chauhan, Yogesh Kumar Meena, Dinesh Gopalani, and Ravi Nahta. 2020. A two-step hybrid unsupervised model with attention mechanism for aspect extraction. Expert Syst. Appl. 161 (2020), 113673.Google ScholarCross Ref
- Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. 2017. Recurrent Attention Network on Memory for Aspect Sentiment Analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9--11, 2017, Martha Palmer, Rebecca Hwa, and Sebastian Riedel (Eds.). Association for Computational Linguistics, 452--461.Google ScholarCross Ref
- Zhiyuan Chen, Arjun Mukherjee, Bing Liu, Meichun Hsu, Malú Castellanos, and Riddhiman Ghosh. 2013. Exploiting Domain Knowledge in Aspect Extraction. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, 18--21 October 2013, Grand Hyatt Seattle, Seattle, Washington, USA, A meeting of SIGDAT, a Special Interest Group of the ACL. ACL, 1655--1667.Google Scholar
- Jinhyuck Choi, Jin-Hee Song, and Yanggon Kim. 2018. An Analysis of Music Lyrics by Measuring the Distance of Emotion and Sentiment. In 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018, Busan, Korea (South), June 27--29, 2018. IEEE Computer Society, 176--181.Google ScholarCross Ref
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2--7, 2019, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171--4186.Google Scholar
- Lorenzo Gatti, Marco Guerini, and Marco Turchi. 2016. SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis. IEEE Trans. Affect. Comput. 7, 4 (2016), 409--421.Google ScholarDigital Library
- Athanasios Giannakopoulos, Claudiu Musat, Andreea Hossmann, and Michael Baeriswyl. 2017. Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA@EMNLP 2017, Copenhagen, Denmark, September 8, 2017, Alexandra Balahur, Saif M. Mohammad, and Erik van der Goot (Eds.). Association for Computational Linguistics, 180--188.Google ScholarCross Ref
- Abdalraouf Hassan and Ausif Mahmood. 2018. Convolutional Recurrent Deep Learning Model for Sentence Classification. IEEE Access 6 (2018), 13949--13957.Google ScholarCross Ref
- Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2017. An Unsupervised Neural Attention Model for Aspect Extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, Regina Barzilay and Min-Yen Kan (Eds.). Association for Computational Linguistics, 388--397.Google Scholar
- Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, August 22--25, 2004, Won Kim, Ron Kohavi, Johannes Gehrke, and William DuMouchel (Eds.). ACM, 168--177.Google ScholarDigital Library
- Niklas Jakob and Iryna Gurevych. 2010. Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, 9--11 October 2010, MIT Stata Center, Massachusetts, USA, A meeting of SIGDAT, a Special Interest Group of the ACL. ACL, 1035--1045.Google ScholarDigital Library
- Rie Johnson and Tong Zhang. 2015. Effective Use of Word Order for Text Categorization with Convolutional Neural Networks. In NAACL.Google Scholar
- Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014. A Convolutional Neural Network for Modelling Sentences. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Baltimore, Maryland, 655--665.Google ScholarCross Ref
- Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Doha, Qatar, 1746--1751.Google ScholarCross Ref
- Artemy Kolchinsky, Nakul Dhande, Kengjeun Park, and Yong-Yeol Ahn. 2017. The Minor Fall, the Major Lift: Inferring Emotional Valence of Musical Chords through Lyrics. CoRR abs/1706.08609 (2017). arXiv:1706.08609Google Scholar
- Quoc V. Le and Tomás Mikolov. 2014. Distributed Representations of Sentences and Documents. In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21--26 June 2014 (JMLR Workshop and Conference Proceedings), Vol. 32. JMLR.org, 1188--1196.Google Scholar
- Xin Li and Wai Lam. 2017. Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9--11, 2017, Martha Palmer, Rebecca Hwa, and Sebastian Riedel (Eds.). Association for Computational Linguistics, 2886--2892.Google ScholarCross Ref
- Qian Liu, Zhiqiang Gao, Bing Liu, and Yuanlin Zhang. 2015. Automated Rule Selection for Aspect Extraction in Opinion Mining. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25--31, 2015, Qiang Yang and Michael J. Wooldridge (Eds.). AAAI Press, 1291--1297.Google Scholar
- Qian Liu, Bing Liu, Yuanlin Zhang, Doo Soon Kim, and Zhiqiang Gao. 2016. Improving Opinion Aspect Extraction Using Semantic Similarity and Aspect Associations. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12--17, 2016, Phoenix, Arizona, USA, Dale Schuurmans and Michael P. Wellman (Eds.). AAAI Press, 2986--2992.Google ScholarDigital Library
- Huaishao Luo, Tianrui Li, Bing Liu, Bin Wang, and Herwig Unger. 2019. Improving Aspect Term Extraction With Bidirectional Dependency Tree Representation. IEEE ACM Trans. Audio Speech Lang. Process. 27, 7 (2019), 1201--1212.Google ScholarDigital Library
- J. MacQueen. 1967. Some Methods for Classification and Analysis of Multivariate Observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability - Vol. 1, L. M. Le Cam and J. Neyman (Eds.). University of California Press, Berkeley, CA, USA, 281--297.Google Scholar
- Tomás Mikolov, Quoc V. Le, and Ilya Sutskever. 2013. Exploiting Similarities among Languages for Machine Translation. CoRR abs/1309.4168 (2013). arXiv:1309.4168Google Scholar
- Martijn Millecamp, Nyi Nyi Htun, Yucheng Jin, and Katrien Verbert. 2018. Controlling Spotify Recommendations: Effects of Personal Characteristics on Music Recommender User Interfaces. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, UMAP 2018, Singapore, July 08--11, 2018, Tanja Mitrovic, Jie Zhang, Li Chen, and David Chin (Eds.). ACM, 101--109.Google ScholarDigital Library
- Tetsuya Nasukawa and Jeonghee Yi. 2003. Sentiment analysis: capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP 2003), October 23--25, 2003, Sanibel Island, FL, USA, John H. Gennari, Bruce W. Porter, and Yolanda Gil (Eds.). ACM, 70--77.Google ScholarDigital Library
- Paolo Pastore, Andrea Iovine, Fedelucio Narducci, and Giovanni Semeraro. 2021. A General Aspect-Term-Extraction Model for Multi-Criteria Recommendations (Long paper). In Joint Workshop Proceedings of the 3rd Edition of Knowledge-aware and Conversational Recommender Systems (KaRS) and the 5th Edition of Recommendation in Complex Environments (ComplexRec) co-located with 15th ACM Conference on Recommender Systems (RecSys 2021), Virtual Event, Amsterdam, The Netherlands, September 25, 2021 (CEUR Workshop Proceedings), Vito Walter Anelli, Pierpaolo Basile, Tommaso Di Noia, Francesco Maria Donini, Cataldo Musto, Fedelucio Narducci, Markus Zanker, Himan Abdollahpouri, Toine Bogers, Bamshad Mobasher, Casper Petersen, and Maria Soledad Pera (Eds.), Vol. 2960. CEUR-WS.org.Google Scholar
- Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. CoRR abs/1802.05365 (2018). arXiv:1802.05365Google Scholar
- Marco Polignano, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro, and Valerio Basile. 2019. AlBERTo: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on Tweets. In Proceedings of the Sixth Italian Conference on Computational Linguistics, Bari, Italy, November 13--15, 2019 (CEUR Workshop Proceedings), Raffaella Bernardi, Roberto Navigli, and Giovanni Semeraro (Eds.), Vol. 2481. CEUR-WS.org.Google Scholar
- Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, Véronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia V. Loukachevitch, Evgeniy V. Kotelnikov, Núria Bel, Salud María Jiménez Zafra, and Gülsen Eryigit. 2016. SemEval-2016 Task 5: Aspect Based Sentiment Analysis. In Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2016, San Diego, CA, USA, June 16--17, 2016, Steven Bethard, Daniel M. Cer, Marine Carpuat, David Jurgens, Preslav Nakov, and Torsten Zesch (Eds.). The Association for Computer Linguistics, 19--30.Google ScholarCross Ref
- Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014. SemEval-2014 Task 4: Aspect Based Sentiment Analysis. In Proceedings of the 8th International Workshop on Semantic Evaluation, SemEval@COLING 2014, Dublin, Ireland, August 23--24, 2014, Preslav Nakov and Torsten Zesch (Eds.). The Association for Computer Linguistics, 27--35.Google ScholarCross Ref
- Soujanya Poria, Erik Cambria, and Alexander F. Gelbukh. 2016. Aspect extraction for opinion mining with a deep convolutional neural network. Knowl. Based Syst. 108 (2016), 42--49.Google ScholarDigital Library
- Marco Pota, Mirko Ventura, Rosario Catelli, and Massimo Esposito. 2021. An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian. Sensors 21, 1 (2021), 133.Google Scholar
- Minchae Song, Hyunjung Park, and Kyung-shik Shin. 2019. Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean. Inf. Process. Manag. 56, 3 (2019), 637--653.Google ScholarDigital Library
- Robert L Thorndike. 1953. Who belongs in the family? Psychometrika 18, 4 (1953), 267--276.Google ScholarCross Ref
- Danny Suarez Vargas, Lucas Rafael Costella Pessutto, and Viviane Pereira Moreira. 2020. Simple Unsupervised Similarity-Based Aspect Extraction. CoRR abs/2008.10820 (2020). arXiv:2008.10820Google Scholar
- Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao. 2016. Attention-based LSTM for Aspect-level Sentiment Classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1--4, 2016, Jian Su, Xavier Carreras, and Kevin Duh (Eds.). The Association for Computational Linguistics, 606--615.Google ScholarCross Ref
- Chuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, and Yongfeng Huang. 2018. A hybrid unsupervised method for aspect term and opinion target extraction. Knowl. Based Syst. 148 (2018), 66--73.Google ScholarCross Ref
- Hai Ye, Zichao Yan, Zhunchen Luo, and Wen-Han Chao. 2017. Dependency-Tree Based Convolutional Neural Networks for Aspect Term Extraction. In Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23--26, 2017, Proceedings, Part II (Lecture Notes in Computer Science), Jinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin, and Yang-Sae Moon (Eds.), Vol. 10235. 350--362.Google Scholar
Index Terms
- Aspect based sentiment analysis in music: a case study with spotify
Recommendations
Sentence compression for aspect-based sentiment analysis
Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as ...
Extracting domain-specific opinion words for sentiment analysis
MICAI'12: Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part IIIn this paper, we consider opinion word extraction, one of the key problems in sentiment analysis. Sentiment analysis (or opinion mining) is an important research area within computational linguistics. Opinion words, which form an opinion lexicon, ...
Aspect and sentiment unification model for online review analysis
WSDM '11: Proceedings of the fourth ACM international conference on Web search and data miningUser-generated reviews on the Web contain sentiments about detailed aspects of products and services. However, most of the reviews are plain text and thus require much effort to obtain information about relevant details. In this paper, we tackle the ...
Comments