Review of sentiment analysis: An emotional product development view

Hong-Bin YAN , Ziyu LI

Front. Eng ›› 2022, Vol. 9 ›› Issue (4) : 592 -609.

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Front. Eng ›› 2022, Vol. 9 ›› Issue (4) : 592 -609. DOI: 10.1007/s42524-022-0227-z
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Review of sentiment analysis: An emotional product development view

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Abstract

Conveying consumers’ specific emotions in new products, referred to as emotional product development or emotional design, is strategically crucial for manufacturers. Given that sentiment analysis (SA) can extract and analyze people’s opinions, sentiments, attitudes, and perceptions regarding different products/services, SA-based emotional design may provide manufacturers with real-time, direct, and rapid decision support. Despite its considerable advancements and numerous survey and review articles, SA is seldom considered in emotional design. This study is among the first efforts to conduct a thorough review of SA from the view of emotional design. The comprehensive review of aspect-level SA reveals the following: 1) All studies focus on extracting product features by mixing technical product features and consumers’ emotional perceptions. Consequently, such studies cannot capture the relationships between technical and emotional attributes and thus cannot convey specific emotions to the new products. 2) Most studies use the English language in SA, but other languages have recently received more interest in SA. Furthermore, after conceptualizing emotion as Kansei and introducing emotional product development and Kansei Engineering, a review of the data-driven emotional design is then conducted. A few efforts start to study emotional design with the help of SA. However, these studies only focus on either analyzing consumers’ preferences on product features or extracting emotional opinions from online reviews, thus cannot realize data-driven emotional product development. Finally, some research opportunities are provided. This study opens a broad door to aspect-level SA and its integration with emotional product development.

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Keywords

sentiment analysis / emotion / product development / Kansei Engineering

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Hong-Bin YAN, Ziyu LI. Review of sentiment analysis: An emotional product development view. Front. Eng, 2022, 9(4): 592-609 DOI:10.1007/s42524-022-0227-z

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References

[1]

Afzaal, M Usman, M Fong, A (2019a). Predictive aspect-based sentiment classification of online tourist reviews. Journal of Information Science, 45( 3): 341–363

[2]

Afzaal, M Usman, M Fong, A (2019b). Tourism mobile app with aspect-based sentiment classification framework for tourist reviews. IEEE Transactions on Consumer Electronics, 65( 2): 233–242

[3]

AhmedMChenQWangY YLiZ H (2019). Hint-embedding attention-based LSTM for aspect identification sentiment analysis. In: Proceedings of the 16th Pacific Rim International Conference on Artificial Intelligence. Cuvu, Yanuka Island: Springer, 569–581

[4]

Akay, A Dragomir, A Erlandsson, B E (2015). Network-based modeling and intelligent data mining of social media for improving care. IEEE Journal of Biomedical and Health Informatics, 19( 1): 210–218

[5]

Akhtar, M S Gupta, D Ekbal, A Bhattacharyya, P (2017). Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis. Knowledge-Based Systems, 125: 116–135

[6]

Al-Smadi, M Qawasmeh, O Al-Ayyoub, M Jararweh, Y Gupta, B (2018). Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. Journal of Computational Science, 27: 386–393

[7]

Al-Smadi, M Talafha, B Al-Ayyoub, M Jararweh, Y (2019). Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. International Journal of Machine Learning and Cybernetics, 10( 8): 2163–2175

[8]

Alaniz, T Biazzo, S (2019). Emotional design: The development of a process to envision emotion-centric new product ideas. Procedia Computer Science, 158: 474–484

[9]

Ali, S Wang, G J Riaz, S (2020). Aspect based sentiment analysis of ridesharing platform reviews for Kansei Engineering. IEEE Access, 8: 173186–173196

[10]

Ali, W Yang, Y W Qiu, X L Ke, Y Q Wang, Y Y (2021). Aspect-level sentiment analysis based on bidirectional-GRU in SIoT. IEEE Access, 9: 69938–69950

[11]

AlqaryoutiOSiyamNAbdel MonemAShaalanK (2020). Aspect-based sentiment analysis using smart government review data. Applied Computing and Informatics, in press,

[12]

Amplayo, R K Lee, S Song, M (2018). Incorporating product description to sentiment topic models for improved aspect-based sentiment analysis. Information Sciences, 454–455: 200–215

[13]

Araque, O Corcuera-Platas, I Sánchez-Rada, J F Iglesias, C A (2017). Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications, 77: 236–246

[14]

Atzeni, M Dridi, A Reforgiato Recupero, D (2018). Using frame-based resources for sentiment analysis within the financial domain. Progress in Artificial Intelligence, 7( 4): 273–294

[15]

Aydin, C R Gungor, T (2020). Combination of recursive and recurrent neural networks for aspect-based sentiment analysis using inter-aspect relations. IEEE Access, 8: 77820–77832

[16]

Bagheri, A Saraee, M de Jong, F (2013). Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Systems, 52: 201–213

[17]

Bagozzi, R P Gopinath, M Nyer, P U (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27( 2): 184–206

[18]

Balters, S Steinert, M (2017). Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices. Journal of Intelligent Manufacturing, 28( 7): 1585–1607

[19]

Barrett, L F Mesquita, B Ochsner, K N Gross, J J (2007). The experience of emotion. Annual Review of Psychology, 58( 1): 373–403

[20]

Basha, S M Rajput, D S (2019). A roadmap towards implementing parallel aspect level sentiment analysis. Multimedia Tools and Applications, 78( 20): 29463–29492

[21]

Birjali, M Kasri, M Beni-Hssane, A (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226: 107134

[22]

Ceron, A Curini, L Iacus, S M (2015). Using sentiment analysis to monitor electoral campaigns: Method matters-evidence from the United States and Italy. Social Science Computer Review, 33( 1): 3–20

[23]

Chatterji, S Varshney, N Rahul, R K (2017). AspectFrameNet: A frameNet extension for analysis of sentiments around product aspects. Journal of Supercomputing, 73( 3): 961–972

[24]

Chauhan, G S Meena, Y K Gopalani, D Nahta, R (2020). A two-step hybrid unsupervised model with attention mechanism for aspect extraction. Expert Systems with Applications, 161: 113673

[25]

Chen, P Sun, Z Q Bing, L D Yang, W (2017). Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Copenhagen: Association for Computational Linguistics, 452–461

[26]

Chen, Y Z Zhuang, T H Guo, K (2021). Memory network with hierarchical multi-head attention for aspect-based sentiment analysis. Applied Intelligence, 51( 7): 4287–4304

[27]

Chiu, M C Lin, K Z (2018). Utilizing text mining and Kansei Engineering to support data-driven design automation at conceptual design stage. Advanced Engineering Informatics, 38: 826–839

[28]

Dahlgaard, J J Schütte, S Ayas, E Mi, Dahlgaard-Park S (2008). Kansei/Affective Engineering design: A methodology for profound affection and attractive quality creation. The TQM Journal, 20( 4): 299–311

[29]

DasuSChaseR (2013). The Customer Service Solution: Managing Emotions, Trust, and Control to Win Your Customer’s Business. New York: McGraw Hill Professional

[30]

Ding, Y Yu, C L Jiang, J (2017). A neural network model for semi-supervised review aspect identification. In: Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining. Jeju: Springer, 668–680

[31]

Do, H H Prasad, P W C Maag, A Alsadoon, A (2019). Deep learning for aspect-based sentiment analysis: A comparative review. Expert Systems with Applications, 118: 272–299

[32]

Eisenman, M (2013). Understanding aesthetic innovation in the context of technological evolution. Academy of Management Review, 38( 3): 332–351

[33]

Elfenbein, H A (2007). Emotion in organizations: A review and theoretical integration. Academy of Management Annals, 1( 1): 315–386

[34]

Elokla, N Hirai, Y Morita, Y (2010). A proposal for measuring user’s Kansei. In: Proceedings of the International Conference on Kansei Engineering and Emotion Research. Paris, 777–788

[35]

Feng, J Z Cai, S Q Ma, X M (2019). Enhanced sentiment labeling and implicit aspect identification by integration of deep convolution neural network and sequential algorithm. Cluster Computing, 22( S3): 5839–5857

[36]

Fu, X H Guo, Y Y Guo, W B Wang, Z Q (2012). Aspect and sentiment extraction based on information-theoretic co-clustering. In: Proceedings of the 9th International Symposium on Neural Networks. Shenyang: Springer, 326–335

[37]

Georgiadou, E Angelopoulos, S Drake, H (2020). Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes. International Journal of Information Management, 51: 102048

[38]

GrimsæthK (2005). Kansei Engineering: Linking Emotions and Product Features. Dissertation for the Bachelor’s Degree. Trondheim: Norwegian University of Science and Technology

[39]

Hai, Z Chang, K Y Cong, G Yang, C C (2015). An association-based unified framework for mining features and opinion words. ACM Transactions on Intelligent Systems and Technology, 6( 2): 1–21

[40]

Hercig, T Brychcín, T Svoboda, L Konkol, M Steinberger, J (2016). Unsupervised methods to improve aspect-based sentiment analysis in Czech. Computación y Sistemas, 20( 3): 365–375

[41]

Hertenstein, J H Platt, M B Veryzer, R W (2005). The impact of industrial design effectiveness on corporate financial performance. Journal of Product Innovation Management, 22( 1): 3–21

[42]

Ho, C S Damien, P Gu, B Konana, P (2017). The time-varying nature of social media sentiments in modeling stock returns. Decision Support Systems, 101: 69–81

[43]

Hsiao, Y H Chen, M C Liao, W C (2017). Logistics service design for cross-border E-commerce using Kansei Engineering with text-mining-based online content analysis. Telematics and Informatics, 34( 4): 284–302

[44]

HuM QLiuB (2004). Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, WA: Association for Computing Machinery, 168–177

[45]

Ishihara, S Ishihara, K Nagamachi, M Matsubara, Y (1997). An analysis of Kansei structure on shoes using self-organizing neural networks. International Journal of Industrial Ergonomics, 19( 2): 93–104

[46]

Jeong, B Yoon, J Lee, J M (2019). Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management, 48: 280–290

[47]

Jiao, Y R Qu, Q X (2019). A proposal for Kansei knowledge extraction method based on natural language processing technology and online product reviews. Computers in Industry, 108: 1–11

[48]

Jiménez-Zafra, S M Martín-Valdivia, M T Martínez-Cámara, E Ureña-López, L A (2016). Combining resources to improve unsupervised sentiment analysis at aspect-level. Journal of Information Science, 42( 2): 213–229

[49]

Jin, J Ji, P Liu, Y Johnson Lim, S C (2015). Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach. Engineering Applications of Artificial Intelligence, 41: 115–127

[50]

Jin, J Liu, Y Ji, P Liu, H G (2016). Understanding big consumer opinion data for market-driven product design. International Journal of Production Research, 54( 10): 3019–3041

[51]

Jurek, A Mulvenna, M D Bi, Y (2015). Improved lexicon-based sentiment analysis for social media analytics. Security Informatics, 4( 1): 9

[52]

Kang, D Park, Y (2014). Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications, 41( 4): 1041–1050

[53]

Kang, Y Zhou, L N (2017). RubE: Rule-based methods for extracting product features from online consumer reviews. Information & Management, 54( 2): 166–176

[54]

Kim, W Ko, T Rhiu, I Yun, M H (2019). Mining affective experience for a Kansei design study on a recliner. Applied Ergonomics, 74: 145–153

[55]

Kraaijeveld, O de Smedt, J (2020). The predictive power of public Twitter sentiment for forecasting cryptocurrency prices. Journal of International Financial Markets, Institutions and Money, 65: 101188

[56]

Krishna, A (2012). An integrative review of sensory marketing: Engaging the senses to affect perception, judgment and behavior. Journal of Consumer Psychology, 22( 3): 332–351

[57]

Krishnan, V Ulrich, K T (2001). Product development decisions: A review of the literature. Management Science, 47( 1): 1–21

[58]

Ladhari, R Souiden, N Dufour, B (2017). The role of emotions in utilitarian service settings: The effects of emotional satisfaction on product perception and behavioral intentions. Journal of Retailing and Consumer Services, 34: 10–18

[59]

Lee, S Harada, A Stappers, P J (2002). TEEN design based on Kansei. In: Green W S, Jordan P W, eds. Pleasure with Products: Beyond Usability. London: CRC Press, 219–229

[60]

Li, Y M Li, T Y (2013). Deriving market intelligence from microblogs. Decision Support Systems, 55( 1): 206–217

[61]

Li, Z Tian, Z G Wang, J W Wang, W M (2020). Extraction of affective responses from customer reviews: An opinion mining and machine learning approach. International Journal of Computer Integrated Manufacturing, 33( 7): 670–685

[62]

Liao, J Wang, S G Li, X L Li, X (2017). FREERL: Fusion relation embedded representation learning framework for aspect extraction. Knowledge-Based Systems, 135: 9–17

[63]

Lin, C H He, Y L Everson, R Ruger, S (2012). Weakly supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data Engineering, 24( 6): 1134–1145

[64]

LiuB (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. New York, NY: Cambridge University Press

[65]

LiuBZhangL (2012). A survey of opinion mining and sentiment analysis. In: Aggarwal C, Zhai C, eds. Mining Text Data. Boston, MA: Springer, 415–463

[66]

Liu, M Z Zhou, F Y Chen, K Zhao, Y (2021). Co-attention networks based on aspect and context for aspect-level sentiment analysis. Knowledge-Based Systems, 217: 106810

[67]

Luo, Z Y Huang, S S Zhu, K Q (2019). Knowledge empowered prominent aspect extraction from product reviews. Information Processing & Management, 56( 3): 408–423

[68]

Lv, Y X Wei, F N Cao, L H Peng, S C Niu, J W Yu, S Wang, C R (2021). Aspect-level sentiment analysis using context and aspect memory network. Neurocomputing, 428: 195–205

[69]

Ma, R X Wang, K Qiu, T Sangaiah, A K Lin, D Liaqat, H B (2019). Feature-based compositing memory networks for aspect-based sentiment classification in social Internet of Things. Future Generation Computer Systems, 92: 879–888

[70]

Maitama, J Z Idris, N Abdi, A Shuib, L Fauzi, R (2020). A systematic review on implicit and explicit aspect extraction in sentiment analysis. IEEE Access, 8: 194166–194191

[71]

Marcacini, R M Rossi, R G Matsuno, I P Rezende, S O (2018). Cross-domain aspect extraction for sentiment analysis: A transductive learning approach. Decision Support Systems, 114: 70–80

[72]

Marstawi, A Sharef, N M Aris, T N M Mustapha, A (2017). Ontology-based aspect extraction for an improved sentiment analysis in summarization of product reviews. In: Proceedings of the 8th International Conference on Computer Modeling and Simulation. Canberra: Association for Computing Machinery, 100–104

[73]

Matsuno, I P Rossi, R G Marcacini, R M Rezende, S O (2016). Aspect-based sentiment analysis using semi-supervised learning in bipartite heterogeneous networks. Journal of Information and Data Management, 7( 2): 141–154

[74]

Miao, Y L Cheng, W F Ji, Y C Zhang, S Kong, Y L (2021). Aspect-based sentiment analysis in Chinese based on mobile reviews for BiLSTM-CRF. Journal of Intelligent & Fuzzy Systems, 40( 5): 8697–8707

[75]

Moghaddam, S Ester, M (2011). ILDA: Interdependent LDA model for learning latent aspects and their ratings from online product reviews. In: Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. Beijing: Association for Computing Machinery, 665–674

[76]

Munezero, M D Montero, C S Sutinen, E Pajunen, J (2014). Are they different? Affect, feeling, emotion, sentiment, and opinion detection in text. IEEE Transactions on Affective Computing, 5( 2): 101–111

[77]

Nagamachi, M (1995). Kansei Engineering: A new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics, 15( 1): 3–11

[78]

Nagamachi, M (2002). Kansei Engineering as a powerful consumer-oriented technology for product development. Applied Ergonomics, 33( 3): 289–294

[79]

Nasim, Z Haider, S (2017). ABSA toolkit: An open source tool for aspect based sentiment analysis. International Journal on Artificial Intelligence Tools, 26( 6): 1750023

[80]

Nguyen, T H Shirai, K (2015). PhraseRNN: Phrase recursive neural network for aspect-based sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Lisbon: Association for Computational Linguistics, 2509–2514

[81]

NormanD A (2004). Emotional Design: Why We Love (or Hate) Everyday Things. New York, NY: Basic Books

[82]

Pai, P F Liu, C H (2018). Predicting vehicle sales by sentiment analysis of Twitter data and stock market values. IEEE Access, 6: 57655–57662

[83]

Petiot, J F Yannou, B (2004). Measuring consumer perceptions for a better comprehension, specification and assessment of product semantics. International Journal of Industrial Ergonomics, 33( 6): 507–525

[84]

Piryani, R Madhavi, D Singh, V K (2017). Analytical mapping of opinion mining and sentiment analysis research during 2000–2015. Information Processing & Management, 53( 1): 122–150

[85]

Poria, S Cambria, E Gelbukh, A (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108: 42–49

[86]

Postrel V (2001). Can good looks really guarantee a product’s success? The New York Times: Economic Scene, 2001–07–12

[87]

Qi, J Y Zhang, Z P Jeon, S Zhou, Y Q (2016). Mining customer requirements from online reviews: A product improvement perspective. Information & Management, 53( 8): 951–963

[88]

Quan, C Q Ren, F J (2014). Unsupervised product feature extraction for feature-oriented opinion determination. Information Sciences, 272: 16–28

[89]

Rana, T A Cheah, Y N (2016). Aspect extraction in sentiment analysis: Comparative analysis and survey. Artificial Intelligence Review, 46( 4): 459–483

[90]

Rana, T A Cheah, Y N (2019). Sequential patterns rule-based approach for opinion target extraction from customer reviews. Journal of Information Science, 45( 5): 643–655

[91]

Rana, T A Cheah, Y N Letchmunan, S (2016). Topic modeling in sentiment analysis: A systematic review. Journal of ICT Research and Applications, 10( 1): 76–93

[92]

Ravi, K Ravi, V (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89: 14–46

[93]

Ray, P Chakrabarti, A (2022). A mixed approach of deep learning method and rule-based method to improve aspect level sentiment analysis. Applied Computing and Informatics, 18( 1/2): 163–178

[94]

Rychalski, A Hudson, S (2017). Asymmetric effects of customer emotions on satisfaction and loyalty in a utilitarian service context. Journal of Business Research, 71: 84–91

[95]

Satterfield, D Kang, S Baer, R Ladjahasan, N (2008). Food as experience a design and evaluation methodology. In: Proceedings of the Design Research Society International Conference. Sheffield, 202

[96]

Schneider, J Hall, J (2011). Idea watch first: Why most product launches fail. Harvard Business Review, 89( 4): 21–23

[97]

Schouten, K Frasincar, F (2014). Finding implicit features in consumer reviews for sentiment analysis. In: Proceedings of the 14th International Conference on Web Engineering. Toulouse: Springer, 130–144

[98]

Schouten, K Frasincar, F (2016). Survey on aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering, 28( 3): 813–830

[99]

Schütte, S T W Eklund, J Axelsson, J R C Nagamachi, M (2004). Concepts, methods and tools in Kansei Engineering. Theoretical Issues in Ergonomics Science, 5( 3): 214–231

[100]

Shen, R P Zhang, H R Yu, H Min, F (2019). Sentiment based matrix factorization with reliability for recommendation. Expert Systems with Applications, 135: 249–258

[101]

Shigemoto, Y (2019). Designing emotional product design: When design management combines engineering and marketing. In: Proceedings of the AHFE International Conference on Affective and Pleasurable Design. Washington, D.C.: Springer, 28–39

[102]

Song, M Park, H Shin, K S (2019). Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean. Information Processing & Management, 56( 3): 637–653

[103]

Suciati, A Budi, I (2019). Aspect-based opinion mining for code-mixed restaurant reviews in Indonesia. In: Proceedings of the 23rd International Conference on Asian Language Processing. Shanghai: IEEE, 59–64

[104]

Thet, T T Na, J C Khoo, C S G (2010). Aspect-based sentiment analysis of movie reviews on discussion boards. Journal of Information Science, 36( 6): 823–848

[105]

Tsang, Y P Wu, C H Lin, K Y Tse, Y K Ho, G T S Lee, C K M (2022). Unlocking the power of big data analytics in new product development: An intelligent product design framework in the furniture industry. Journal of Manufacturing Systems, 62: 777–791

[106]

Tubishat, M Idris, N Abushariah, M A M (2018). Implicit aspect extraction in sentiment analysis: Review, taxonomy, opportunities, and open challenges. Information Processing & Management, 54( 4): 545–563

[107]

Turney, P D Littman, M L (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 21( 4): 315–346

[108]

UI Hassan, A Hussain, J Hussain, M Sadiq, M Lee, S (2017). Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression. In: Proceedings of the International Conference on Information and Communication Technology Convergence. Jeju: IEEE, 138–140

[109]

Venugopalan, M Gupta, D Bhatia, V (2021). A supervised approach to aspect term extraction using minimal robust features for sentiment analysis. In: Proceedings of Progress in Advanced Computing and Intelligent Engineering. Bhubaneswar: Springer, 237–251

[110]

Verganti, R (2006). Innovating through design. Harvard Business Review, 84: 114–122

[111]

Wan, Y Nie, H Z R Lan, T G Wang, Z H (2015). Fine-grained sentiment analysis of online reviews. In: Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery. Zhangjiajie: IEEE, 1406–1411

[112]

Wang, T Cai, Y Leung, H F Lau, R Y K Li, Q Min, H Q (2014). Product aspect extraction supervised with online domain knowledge. Knowledge-Based Systems, 71: 86–100

[113]

Wang, W Tan, G Y Wang, H W (2017a). Cross-domain comparison of algorithm performance in extracting aspect-based opinions from Chinese online reviews. International Journal of Machine Learning and Cybernetics, 8( 3): 1053–1070

[114]

Wang, W Xu, H Wan, W (2013). Implicit feature identification via hybrid association rule mining. Expert Systems with Applications, 40( 9): 3518–3531

[115]

Wang, W M Li, Z Tian, Z G Wang, J W Cheng, M N (2018). Extracting and summarizing affective features and responses from online product descriptions and reviews: A Kansei text mining approach. Engineering Applications of Artificial Intelligence, 73: 149–162

[116]

Wang, W M Wang, J W Li, Z Tian, Z G Tsui, E (2019a). Multiple affective attribute classification of online customer product reviews: A heuristic deep learning method for supporting Kansei Engineering. Engineering Applications of Artificial Intelligence, 85: 33–45

[117]

WangW YPanS JDahlmeierDXiaoX K (2017b). Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, CA: AAAI Press, 3316–3322

[118]

Wang, X D Tang, M W Yang, T Wang, Z (2021). A novel network with multiple attention mechanisms for aspect-level sentiment analysis. Knowledge-Based Systems, 227: 107196

[119]

WangY QSunA XHuangM LZhuX Y (2019b). Aspect-level sentiment analysis using AS-Capsules. In: Proceedings of the World Wide Web Conference. San Francisco, CA: Association for Computing Machinery, 2033–2044

[120]

Wang, Y Y Chen, Q Ahmed, M Li, Z H Pan, W Liu, H L (2019c). Joint inference for aspect-level sentiment analysis by deep neural networks and linguistic hints. IEEE Transactions on Knowledge and Data Engineering, 33( 5): 2002–2014

[121]

Wu, C Xiong, Q Y Yang, Z Y Gao, M Li, Q D Yu, Y Wang, K G Zhu, Q W (2021). Residual attention and other aspects module for aspect-based sentiment analysis. Neurocomputing, 435: 42–52

[122]

Xiao, S S Wei, C P Dong, M (2016). Crowd intelligence: Analyzing online product reviews for preference measurement. Information & Management, 53( 2): 169–182

[123]

Xu, H Zhang, F Wang, W (2015). Implicit feature identification in Chinese reviews using explicit topic mining model. Knowledge-Based Systems, 76: 166–175

[124]

Xu, Q N Zhu, L Dai, T Guo, L Cao, S S (2020). Non-negative matrix factorization for implicit aspect identification. Journal of Ambient Intelligence and Humanized Computing, 11( 7): 2683–2699

[125]

Xu, X K Cheng, X Q Tan, S B Liu, Y Shen, H W (2013). Aspect-level opinion mining of online customer reviews. China Communications, 10( 3): 25–41

[126]

Yadav, A Vishwakarma, D K (2020). Sentiment analysis using deep learning architectures: A review. Artificial Intelligence Review, 53( 6): 4335–4385

[127]

Yadav, M L Roychoudhury, B (2019). Effectiveness of domain-based lexicons vis-a-vis general lexicon for aspect-level sentiment analysis: A comparative analysis. Journal of Information & Knowledge Management, 18( 3): 1950033

[128]

Yan, H B Li, M (2021). An uncertain Kansei Engineering methodology for behavioral service design. IISE Transactions, 53( 5): 497–522

[129]

Yan, H B Ma, T J (2015). A fuzzy group decision making approach to new product concept screening at the fuzzy front end. International Journal of Production Research, 53( 13): 4021–4049

[130]

Yan, H B Ma, T J Sriboonchitta, S Huynh, V N (2017). A stochastic dominance based approach to consumer-oriented Kansei evaluation with multiple priorities. Annals of Operations Research, 256( 2): 329–357

[131]

Ye, X X Xu, Y Luo, M S (2021). ALBERTC-CNN based aspect level sentiment analysis. IEEE Access, 9: 94748–94755

[132]

Yu, J F Jiang, J Xia, R (2019). Global inference for aspect and opinion terms co-extraction based on multi-task neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27( 1): 168–177

[133]

Yu, X H Liu, Y Huang, J X An, A J (2012). Mining online reviews for predicting sales performance: A case study in the movie domain. IEEE Transactions on Knowledge and Data Engineering, 24( 4): 720–734

[134]

Zhang, W H Xu, H Wan, W (2012). Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications, 39( 11): 10283–10291

[135]

Zhang, Y J Xu, B Zhao, T J (2020). Convolutional multi-head self-attention on memory for aspect sentiment classification. IEEE/CAA Journal of Automatica Sinica, 7( 4): 1038–1044

[136]

Zheng, X L Lin, Z Wang, X W Lin, K J Song, M N (2014). Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification. Knowledge-Based Systems, 61: 29–47

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