Automatic Aspect-Based Sentiment Summarization for Visual, Structured, and Textual Summaries
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Abstract
Online reviews are valuable sources of information to help companies to make good decisions for business intelligence. In this study, we propose an Automatic Aspect-based Sentiment Summarization (AAbSS) system that has two components and can generate a summary as an output. The first component is the Aspect-based Knowledge Representation and Selection (AKRS) used to represent reviews based on aspects and their polarities for selecting aspect-based knowledge. To represent and selection knowledge, a set of frequency of polarity opinion strength, a summation of frequency of aspect, and an information of aspect are initiated. The second component is the Summary Format Generation (SFG) used to automatically generate three kinds of formats. In this component (SFG), new representations for visual and structured summaries, and a new way of applied natural language generation for a textual summary are proposed. In the experiments, 13 domains from benchmark datasets of customer reviews, e.g. cell phone, digital camera, etc. are used. The proposed system not only fast generates summaries having good performance when compared to other summaries generated by other systems and easily updated when adding new reviews in the same domain but also does not spend memory capacity to save any raw data.
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REFERENCES
T. Chumwatana, "Comment analysis for product and service satisfaction from Thai customers’ review in social network," J. of Inform. and Commun. Technology, vol. 17, pp. 271-289, 2018.
N. Samsudin, M. Puteh, A. R. Hamdan, and M. Z. A. Nazri, "Normalization of noisy texts in Malaysian online reviews," J. of Inform. and Commun. Technology, vol. 12, pp. 147–159, 2013.
N. Dedić and C. Stanier, "Measuring the success of changes to existing business intelligence solutions to improve business intelligence reporting," in Proc. 2016 Int. Conf. Research and Practical Issues of Enterprise Inform. Syst., Cham, 2016, pp. 225-236.
W. Maharani, D. H. Widyantoro, and M. L. Khodra, "Aspect-based opinion summarization: A survey," J. of Theoretical and Applied Inform. Technology, vol. 95, pp. 448-456, 2017.
R. E. López Condori and T. A. Salgueiro Pardo, "Opinion summarization methods: Comparing and extending extractive and abstractive approaches," Expert Systems with Applicat., vol. 78, pp. 124-134, 2017.
M. Hu and B. Liu, "Opinion feature extraction using class sequential rules," in Proc. 2006 AAAI Spring Symp., Technical Report SS-06-03, USA, 2006, pp. 61-66.
G. Carenini and L. Rizoli, "A multimedia interface for facilitating comparisons of opinions," in Proc. 14th Int. Conf. Intell. user interfaces, USA, 2009.
M. Abulaish, Jahiruddin, M. N. Doja, and T. Ahmad, "Feature and opinion mining for customer review summarization," in Pattern Recognition and Machine Intell., Heidelberg, 2009, pp. 219-224.
P. Kherwa, A. Sachdeva, D. Mahajan, N. Pande, and P. K. Singh, "An approach towards comprehensive sentimental data analysis and opinion mining," in Proc. 2014 IEEE Int. Advance Computing Conf. (IACC), 2014, pp. 606-612.
A. Kamal, "Review mining for feature based opinion summarization and visualization," CoRR, vol. abs/1504.03068, 2015.
H. Wu, Y. Gu, S. Sun, and X. Gu, "Aspect-based opinion summarization with convolutional neural networks," in Proc. 2016 Int. Joint Conf. on Neural Networks (IJCNN), 2016, pp. 3157-3163.
D. Kanbur and M. S. Aktas, "Ontology based aspect oriented opinion summary methodology," in Proc. 5th Int. Conf. on Building and Exploring Web Based Environments (WEB), Spain, 2017, pp. 7-16.
M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proc. 10th ACM SIGKDD Int. Conf. on Knowledge discovery and data mining, USA, 2004, pp. 168-177.
L. Zhuang, F. Jing, and X.-Y. Zhu, "Movie review mining and summarization," in Proc. 15th Int. Conf. on Inform. and knowledge manage., USA, 2006.
S. Blair-goldensohn, T. Neylon, K. Hannan, G. A. Reis, R. Mcdonald, and J. Reynar, "Building a sentiment summarizer for local service reviews," in NLP in the Inform. Explosion Era, 2008.
D. K. Ly, K. Sugiyama, Z. Lin, and M.Y. Kan, "Product review summarization from a deeper perspective," in Proc. 11th Annu. Int. ACM/IEEE joint Conf. on Digital libraries, Canada, 2011.
C. Liu, W. Hsaio, C. Lee, G. Lu, and E. Jou, "Movie rating and review summarization in mobile environment," IEEE Trans.on Syst., Man, and Cybernetics, Part C (Applicat. and Reviews), vol. 42, pp. 397-407, 2012.
J. Jmal and R. Faiz, "Customer review summarization approach using Twitter and SentiWordNet," in Proc. 3rd Int. Conf. on Web Intell., Mining and Semantics, Spain, 2013.
K. Bafna and D. Toshniwal, "Feature based summarization of customers’ reviews of online products," in Procedia Comput. Sci., vol. 22, pp. 142-151, 2013.
K. Yauris and M. L. Khodra, "Aspect-based summarization for game review using double propagation," in Proc. 2017 Int. Conf. on Advanced Informatics, Concepts, Theory, and Applicat. (ICAICTA), 2017, pp. 1-6.
R. K. Amplayo and M. Song, "An adaptable fine-grained sentiment analysis for summarization of multiple short online reviews," Data & Knowledge Eng., vol. 110, pp. 54-67, 2017.
I. Titov and R. McDonald, "Modeling online reviews with multi-grain topic models," in Proc. 17th Int. Conf. on World Wide Web, China, 2008.
Y. Lu, C. Zhai, and N. Sundaresan, "Rated aspect summarization of short comments," in Proc. 18th Int. Conf. on World wide web, Spain, 2009.
X. Xu, T. Meng, and X. Cheng, "Aspect-based extractive summarization of online reviews," in Proc. 2011 ACM Symp. on Applied Computing, Taiwan, 2011.
G. Carenini, J. C. K. Cheung, and A. Pauls, "Multi-document summarization of evaluative text," Computational Intell., vol. 29, pp. 545-576, 2013.
N. Yu, M. Huang, Y. Shi, and X. Zhu, "Product review summarization by exploiting phrase properties," in Proc. 26th Int. Conf. on Computational Linguistics: Tech. Papers (COLING), Japan, 2016, pp. 1113–1124.
S. Angelidis and M. Lapata, "Summarizing opinions: Aspect extraction meets sentiment prediction and they are both weakly supervised," in Proc. 2018 Conf. on Empirical Methods in Natural Language Processing (EMNLP), 2018.
T. A. Tran, J. Duangsuwan, and W. Wettayaprasit, "A novel automatic sentiment summarization from aspect-based customer reviews," in Proc. 15th Int. Joint Conf. on Comput. Sci. and Software Eng. (JCSSE), 2018, pp. 1-6.
S. Gerani, Y. Mehdad, G. Carenini, R. T. Ng, and B. Nejat, "Abstractive summarization of product reviews using discourse structure," in Proc. 2014 Conf. on Empirical Methods in Natural Language Processing (EMNLP), Qatar, 2014, pp. 1602–1613.
M. Yang, Q. Qu, Y. Shen, Q. Liu, W. Zhao, and J. Zhu, "Aspect and sentiment aware abstractive review summarization," in Proc. 27th Int. Conf. on Computational Linguistics (COLING), USA, 2018.
S. Gerani, G. Carenini, and R. T. Ng, "Modeling content and structure for abstractive review summarization," Comput. Speech & Language, vol. 53, pp. 302-331, 2019.
Awario. Awario. Available: https://awario.com
/demo/. Last Accessed: May 13, 2020.
Hootsuite-Insights. Hootsuite Insights. Available: https://hootsuite.com/. Last Accessed: May 13, 2020.
P. Grover, A. K. Kar, and P. V. Ilavarasan, "Impact of corporate social responsibility on reputation—insights from tweets on sustainable development goals by CEOs," Int. J. of Inform. Manage., vol. 48, pp. 39-52, 2019.
S. Viz. Tweet Sentiment Visualization. Available: https://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/. Last Accessed: May 13, 2020.
J. Lertsiwaporn and T. Senivongse, "Time-based visualization tool for topic modeling and sentiment analysis of Twitter messages," in Proc. 2017 Int. MultiConf. of Eng. and Comput. Scientists (IMECS), Hong Kong, 2017.
Social-Mention. Social mention. Available: http://www.socialmention.com/. Last Accessed: May 13, 2020.
R. T. Khasawneh, H. A. Wahsheh, M. N. Al-Kabi, and I. M. Alsmadi, "Sentiment analysis of arabic social media content: A comparative study," in Proc. 8th Int. Conf. for Internet Technology and Secured Trans. (ICITST), 2013, pp. 101-106.
J. J. Meyers, D. L. Hansen, J. S. Giboney, and D. C. Rowe, "Training future cybersecurity professionals in spear phishing using SiEVE," in Proc. 19th Annu. SIG Conf. on Inform. Technology Educ., USA, 2018.
C. Borges, J. Araújo, and A. Rodrigues, "Towards an approach to elicit domain requirements from social networks: The case of emergency systems," in Proc. 33rd Annu. ACM Symp. on Applied Computing, France, 2018.
Social-Searcher. Social media search & monitoring API. Available: http://www.social-searcher.com/. Last Accessed: May 13, 2020.
Talkwalker. Talkwalker. Available: https://www.talkwalker.com. Last Accessed: May 13, 2020.
Z. Asghar, T. Ali, I. Ahmad, S. Tharanidharan, S. K. A. Nazar, and S. Kamal, "Sentiment analysis on automobile brands using Twitter data," in Intell. Technologies and Applicat., Singapore, 2019, pp. 76-85.
SentiStrength. SentiStrength. Available: http://sentistrength.wlv.ac.uk/. Last Accessed: May 13, 2020.
T. Baviera, A. Sampietro, and F. J. García-Ull, "Political conversations on Twitter in a disruptive scenario: The role of “party evangelists” during the 2015 Spanish general elections," The Commun. Review, vol. 22, pp. 117-138, 2019.
R. Feldman, B. Rozenfeld, R. Bar-Haim, and M. Fresko, "The Stock Sonar - sentiment analysis of stocks based on a hybrid approach," in Proc. 23rd Conf. on Innovative Applicat. of Artificial Intell. (IAAI), USA, 2011.
T. P. Lau, S. Wang, Y. Man, C. F. Yuen, and I. King, "Language technologies for enhancement of teaching and learning in writing," in Proc. 23rd Int. Conf. on World Wide Web, Korea, 2014.
Lexalytics. Lexalytics: Data analytics with NLP & text analytics. Available: https://
www.lexalytics.com/. Last Accessed: May 13, 2020.
Meaning-Cloud. Meaning cloud. Available: https://www.meaningcloud.com/. Last Accessed: May 13, 2020.
T. Swoboda, M. Kaufmann, and M. L. Hemmje, "Toward cloud-based classification and annotation support," in Proc. 6th Int. Conf. on Cloud Computing and Services Sci., Vol. 1 and 2, Italy, 2016.
F. Z. Ennaji, A. E. Fazziki, M. Sadgal, and D. Benslimane, "Social intelligence framework: Extracting and analyzing opinions for social CRM," in Proc. 12th Int. Conf. of Comp. Syst. and Applicat. (AICCSA), 2015, pp. 1-7.
Sentiment-Analyzer. Sentiment analyzer. Available: https://www.danielsoper.com/
sentimentanalysis/default.aspx. Last Accessed: May 13, 2020.
M. Pontiki, D. Galanis, H. Papageorgiou, S. Manandhar, and I. Androutsopoulos, "SemEval-2015 task 12: Aspect based sentiment analysis," in Proc. 9th Int. Workshop on Semantic Evaluation (SemEval 2015), 2015, pp. 486-495.
Q. Liu, Z. Gao, B. Liu, and Y. Zhang, "Automated rule selection for aspect extraction in opinion mining," in Proc. 24th Int. Conf. on Artificial Intell., Argentina, 2015.