More Web Proxy on the site http://driver.im/

research-article

Leveraging semantic features for recommendation: : Sentence-level emotion analysis

Authors:

Penny SweetserAuthors Info & Claims

Volume 58, Issue 3

https://doi.org/10.1016/j.ipm.2021.102543

Published: 01 May 2021 Publication History

Highlights

•

A review-based recommendation model is proposed to separate user reviews into different sentiment orientations.

•

A voting mechanism is proposed to directly generate the recommendation set through assigning voting rights according to similarities.

•

The effectiveness of separating user reviews into positive and negative feedbacks is validated in the experiments.

Abstract

Personalized recommendation systems can help users to filter redundant information from a large amount of data. Previous relevant researches focused on learning user preferences by analyzing texts from comment communities without exploring the detailed sentiment polarity, which encountered the cold-start problem. To address this research gap, we propose a hybrid personalized recommendation model that extracts user preferences by analyzing user review content in different sentiment polarity at the sentence level, based on jointly applying user-item score matrices and dimension reduction methods. A novel voting mechanism is also designed based on positive preferences from the neighbors of the target user to directly generate the recommendation results. The experimental results of testing the proposed model with a real-world data set show that our proposed model can achieve better recommendation effects than the representative recommendation algorithms. In addition, we demonstrated that fine-grained emotion recognition has good adaptability to a sparse rating matrix with a reasonable and good performance.

References

[1]

K. Abdelaziz, O. Samir, C. Chemseddine, Recommendations-based on semantic analysis of social networks in learning environments, Computers in Human Behavior (2018).

[2]

M. Aharon, O. Anava, N. Avigdor-Elgrabli, D. Drachsler-Cohen, S. Golan, O. Somekh, Excuseme: Asking users to help in item cold-start recommendations, in: Proceedings of the 9th ACM Conference on Recommender Systems, ACM, 2015, pp. 83–90.

[3]

Y. Bao, H. Fang, J. Zhang, Topicmf: Simultaneously exploiting ratings and reviews for recommendation, in: Twenty-Eighth AAAI conference on artificial intelligence, 2014.

[4]

I. Barjasteh, R. Forsati, F. Masrour, A.H. Esfahanian, H. Radha, Cold-Start Item and User Recommendation with Decoupled Completion and Transduction, in: ACM Conference on Recommender Systems, 2015.

[5]

K. Bhargava, T. Gujral, M. Chawla, T. Gujral, Comment based Seller Trust model for E-commerce, in: 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), IEEE, 2016, pp. 387–391.

[6]

D. Cai, Q. Mei, J. Han, C. Zhai, Modeling hidden topics on document manifold, in: Proceedings of the 17th ACM conference on Information and knowledge management, ACM, 2008, pp. 911–920.

[7]

L.A.G. Camacho, S.N. Alves-Souza, Social network data to alleviate cold-start in recommender system: A systematic review, Information Processing & Management 54 (4) (2018) 529–544.

Digital Library

[8]

B.R. Cami, H. Hassanpour, H. Mashayekhi, User preferences modeling using dirichlet process mixture model for a content-based recommender system, Knowledge-Based Systems 163 (2019) 644–655.

[9]

M.H. Chen, C.H. Teng, P.C. Chang, Applying artificial immune systems to collaborative filtering for movie recommendation, Advanced Engineering Informatics 29 (4) (2015) 830–839.

[10]

F. Colace, M. De Santo, L. Greco, V. Moscato, A. Picariello, A collaborative user-centered framework for recommending items in online social networks, Computers in Human Behavior 51 (2015) 694–704. OCT.

[11]

R.G. Crespo, O.S. Martinez, J.M.C. Lovelle, B.C.P. Garcia-Bustelo, J.E.L. Gayo, P.O.D. Pablos, Recommendation system based on user interaction data applied to intelligent electronic books, Computers in Human Behavior 27 (4) (2011) 1445–1449.

[12]

C. Dupuy, F. Bach, C. Diot, Qualitative and descriptive topic extraction from movie reviews using lda, in: International Conference on Machine Learning and Data Mining in Pattern Recognition, Springer, 2017, pp. 91–106.

[13]

J. Goldberger, S. Gordon, H. Greenspan, An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures. Computer Vision, in: Proceedings of the Ninth IEEE International Conference on, IEEE, 2003.

[14]

V. Hatzivassiloglou, K.R. McKeown, Predicting the semantic orientation of adjectives, in: Proceedings of the 35th annual meeting of the association for computational linguistics and eighth conference of the european chapter of the association for computational linguistics, Association for Computational Linguistics, 1997, pp. 174–181.

[15]

I. Heimbach, J. Gottschlich, O. Hinz, The value of user's Facebook profile data for product recommendation generation, Electronic Markets 25 (2) (2015) 125–138.

[16]

M. Hoang, O.A. Bihorac, J. Rouces, Aspect-Based Sentiment Analysis using BERT, in: Proceedings of the 22nd Nordic Conference on Computational Linguistics, 2019.

[17]

P.Y. Hsu, H.T. Lei, S.H. Huang, T.H. Liao, Y.C. Lo, C.C. Lo, Effects of sentiment on recommendations in social network, Electronic Markets 29 (2) (2019) 253–262.

[18]

Y. Hu, Y. Koren, C. Volinsky, Collaborative filtering for implicit feedback datasets, in: Proceedings of the 8th IEEE international conference on data mining, IEEE, 2008.

[19]

H. Huang, H. Shen, Z. Meng, Item diversified recommendation based on influence diffusion, Information Processing & Management 56 (3) (2019) 939–954.

Digital Library

[20]

X. Huang, S.Z. Li, Y. Wang, Jensen-Shannon boosting learning for object recognition, in: IEEE Computer Society Conference on Computer Vision & Pattern Recognition, 2005.

[21]

Y. Koren, R. Bell, C. Volinsky, Matrix factorization techniques for recommender systems, Computer (8) (2009) 30–37.

Digital Library

[22]

V. Kumar, A.K. Pujari, S.K. Sahu, V.R. Kagita, V. Padmanabhan, Collaborative filtering using multiple binary maximum margin matrix factorizations, Information Sciences 380 (2017) 1–11.

[23]

D.D. Lee, H.S. Seung, Learning the parts of objects by non-negative matrix factorization, Nature 401 (6755) (1999) 788.

[24]

D.R. Liu, K.Y. Chen, Y.C. Chou, J.H. Lee, Online recommendations based on dynamic adjustment of recommendation lists, Knowledge-Based Systems 161 (2018) 375–389.

[25]

Y. Lu, R. Dong, B. Smyth, Coevolutionary recommendation model: Mutual learning between ratings and reviews, in: Proceedings of the 2018 World Wide Web Conference International World Wide Web Conferences Steering Committee, 2018, pp. 773–782.

[26]

Y. Lyu, C.Y. Chow, R. Wang, V.C. Lee, iMCRec: A multi-criteria framework for personalized point-of-interest recommendations, Information Sciences 483 (2019) 294–312.

[27]

Ma, C. (2008). A guide to singular value decomposition for collaborative filtering. Techreport. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.571.6274&rep=rep1&type=pdf.

[28]

A. Martí Bigorra, O. Isaksson, M. Karlberg, Aspect-based Kano categorization, International Journal of Information Management 46 (2019) 163–172.

[29]

J. McAuley, J. Leskovec, Hidden factors and hidden topics: Understanding rating dimensions with review text, in: Proceedings of the 7th ACM conference on Recommender systems (RecSys ’13), ACM, 2013, pp. 165–172.

[30]

W. Medhat, A. Hassan, H. Korashy, Sentiment analysis algorithms and applications: A survey, Ain Shams Engineering Journal 5 (4) (2014) 1093–1113.

[31]

M.K. Najafabadi, M.N. Mahrin, S. Chuprat, H.M. Sarkan, Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data, Computers in Human Behavior 67 (2017) 113–128.

Digital Library

[32]

V. Parkhe, B. Biswas, Sentiment analysis of movie reviews: Finding most important movie aspects using driving factors, Soft Computing 20 (9) (2016) 3373–3379.

[33]

D.H. Pham, A.C. Le, T.K.C. Le, Learning word embeddings for aspect-based sentiment analysis, Computational Linguistics (2018).

[34]

G. Pitsilis, W. Wang, Harnessing the power of social bookmarking for improving tag-based recommendations, Computers in Human Behavior 50 (2015) 239–251.

[35]

R. Salakhutdinov, A. Mnih, Probabilistic matrix factorization, In Advances in neural information processing systems (2008) 1257–1264.

[36]

J. Sanz-Cruzado, P. Castells, C. Macdonald, I. Ounis, Effective contact recommendation in social networks by adaptation of information retrieval models, Information Processing & Management 57 (5) (2020).

[37]

S. Seo, J. Huang, H. Yang, Y. Liu, Interpretable convolutional neural networks with dual local and global attention for review rating prediction, in: Proceedings of the Eleventh ACM Conference on Recommender Systems, ACM, 2017, pp. 297–305.

[38]

L. Shengli, L. Fan, The interaction effects of online reviews and free samples on consumers’ downloads: An empirical analysis, Information Processing & Management 56 (6) (2019).

[39]

H. Steck, Evaluation of recommendations: Rating-prediction and ranking, in: ACM Conference on Recommender Systems, 2013.

[40]

D. Tang, Z. Zhang, Y. He, C. Lin, D. Zhou, Hidden topic–emotion transition model for multi-level social emotion detection, Knowledge-Based Systems 164 (2019) 426–435.

[41]

H. Wang, D. Amagata, T. Maekawa, T. Hara, M. Kurokawa, Preliminary investigation of alleviating user cold-start problem in e-commerce with deep cross-domain recommender system, in: Companion Proceedings of the 2019 World Wide Web Conference, ACM, 2019, pp. 398–403.

[42]

J. Wang, Y. Fan, L. Feng, Z. Ye, H. Zhang, Research hotspot prediction and regular evolutionary pattern identification based on NSFC grants using NMF and semantic retrieval, IEEE access : Practical Innovations, Open Solutions 99 (2019) 1-1.

[43]

J. Wei, J. He, K. Chen, Y. Zhou, Z. Tang, Collaborative filtering and deep learning based recommendation system for cold start items, Expert Systems with Applications 69 (2017) 29–39.

[44]

S. Wei, X. Zheng, D. Chen, C. Chen, A hybrid approach for movie recommendation via tags and ratings, Electronic Commerce Research & Applications 18 (C) (2016) 83–94.

[45]

J. Weng, E.P. Lim, J. Jiang, Q. He, TwitterRank: Finding topic-sensitive influential twitterers, in: Proceedings of the 3rd International ACM Conference on Web Search and Data Mining, 2010.

[46]

Y. Xia, J. Liu, H. Li, An adaptive inertia weight particle swarm optimization algorithm for IIR digital filter, in: International Conference on Artificial Intelligence & Computational Intelligence, 2010.

[47]

D. Xiao, Y. Ji, Y. Li, F. Zhuang, C. Shi, Coupled matrix factorization and topic modeling for aspect mining, Information Processing & Management 54 (6) (2018) 861–873.

[48]

C. Xu, A novel recommendation method based on social network using matrix factorization technique, Information Processing & Management 54 (3) (2018) 463–474.

Digital Library

[49]

K. Xu, X. Zheng, Y. Cai, H. Min, Z. Gao, B. Zhu, et al., Improving user recommendation by extracting social topics and interest topics of users in uni-directional social networks, Knowledge-Based Systems 140 (2018) 120–133.

Digital Library

[50]

L. Yang, H. Lin, Construction and application of Chinese emotional corpus, Workshop on Chinese lexical semantics, Springer, Berlin, Heidelberg, 2012, pp. 122–133.

[51]

W. Zhang, G. Ding, L. Chen, C. Li, C. Zhang, Generating virtual ratings from chinese reviews to augment online recommendations, ACM Transactions on Intelligent Systems & Technology 4 (1) (2013) 1–17.

[52]

W. Zhang, J. Wang, Integrating topic and latent factors for scalable personalized review-based rating prediction, IEEE Educational Activities Department (2016).

[53]

Z. Zhao, Q. Yang, H. Lu, T. Weninger, D. Cai, X. He, et al., Social-aware movie recommendation via multimodal network learning, IEEE Transactions on Multimedia (99) (2017) PP1-1.

[54]

F. Zhuang, X. Li, X. Jin, D. Zhang, L. Qiu, Q. He, Semantic feature learning for heterogeneous multitask classification via non-negative matrix factorization, IEEE Transactions on Cybernetics 48 (8) (2018) 2284–2293.

Cited By

Aldous KSalminen JFarooq AJung SJansen B(2024)Using ChatGPT in Content Marketing: Enhancing Users’ Social Media Engagement in Cross-Platform Content Creation through Generative AIProceedings of the 35th ACM Conference on Hypertext and Social Media10.1145/3648188.3675142(376-383)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1145/3648188.3675142
Białas MMirończuk MMańdziuk J(2024)Leveraging spiking neural networks for topic modelingNeural Networks10.1016/j.neunet.2024.106494178:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106494
Ou WHuynh V(2024)Aspect-level item recommendation based on user reviews with variational autoencodersInformation Sciences: an International Journal10.1016/j.ins.2024.120655671:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120655
Show More Cited By

Recommendations

Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation
WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

Previous research on Recommender Systems (RS), especially the continuously popular approach of Collaborative Filtering (CF), has been mostly focusing on the information resource of explicit user numerical ratings or implicit (still numerical) feedbacks. ...
Leveraging user libraries to bootstrap collaborative filtering
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

We introduce a novel graphical model, the collaborative score topic model (CSTM), for personal recommendations of textual documents. CSTM's chief novelty lies in its learned model of individual libraries, or sets of documents, associated with each user. ...
Research on Collaborative Filtering Recommendation Algorithm Based on Sentiment Analysis and Topic Model
ICBDC '19: Proceedings of the 4th International Conference on Big Data and Computing

At present, the research about the personalized recommendation algorithm is very popular, there are two problems in this kind of algorithm. First, in the pre-filling stage, user preferences cannot be expressed only by scores, because only using the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 58, Issue 3

May 2021

1030 pages

ISSN:0306-4573

Issue’s Table of Contents

Copyright © 2021.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 May 2021

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Aldous KSalminen JFarooq AJung SJansen B(2024)Using ChatGPT in Content Marketing: Enhancing Users’ Social Media Engagement in Cross-Platform Content Creation through Generative AIProceedings of the 35th ACM Conference on Hypertext and Social Media10.1145/3648188.3675142(376-383)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1145/3648188.3675142
Białas MMirończuk MMańdziuk J(2024)Leveraging spiking neural networks for topic modelingNeural Networks10.1016/j.neunet.2024.106494178:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106494
Ou WHuynh V(2024)Aspect-level item recommendation based on user reviews with variational autoencodersInformation Sciences: an International Journal10.1016/j.ins.2024.120655671:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120655
Pandey AVishwakarma D(2024)Progress, achievements, and challenges in multimodal sentiment analysis using deep learningApplied Soft Computing10.1016/j.asoc.2023.111206152:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111206
Pandey AVishwakarma D(2023)VABDC-NetKnowledge-Based Systems10.1016/j.knosys.2023.110515269:COnline publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110515
Jing ELiu YChai YSun JSamtani SJiang YQian Y(2023)A deep interpretable representation learning method for speech emotion recognitionInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10350160:6Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103501
Gan MMa Y(2023)Mapping user interest into hyper-spherical spaceInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10316960:2Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.ipm.2022.103169
Long XJin T(2023)Prompt Tuning Models on Sentiment-Aware for Explainable RecommendationCognitive Computing – ICCC 202310.1007/978-3-031-51671-9_9(116-132)Online publication date: 17-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-51671-9_9
Aldous KAn JJansen B(2022)Measuring 9 Emotions of News Posts from 8 News Organizations across 4 Social Media Platforms for 8 MonthsACM Transactions on Social Computing10.1145/35164914:4(1-31)Online publication date: 23-Mar-2022
https://dl.acm.org/doi/10.1145/3516491
Ding HLiu QHu G(2022)TDTMFInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10303759:5Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.103037
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents