Product review sites are widespread on the Internet and are rapidly gaining in popularity among consumers. This already large volume of user-generated content is dramatically growing every day, making it hard for consumers to filter out the worthwhile information which appears on the various review sites. There commendation system plays a significant role in solving the problem of information overload. This study proposes a framework which integrates a collaborative filtering approach and an opinion mining technique for movie recommendation. Within the proposed framework, sentiment analysis is first applied to the users’ reviews to detect consumer opinions about the movie they have watched and to explore the individual’s preference profile. Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of ‘data sparsity’. Experimental results obtained from real online reviews show that our proposed method is effective in dealing with insufficient data and is more accurate and efficient than existing traditional methods.
GaikarDDMarakarkandyBDasguptaC. Using Twitter data to predict the performance of Bollywood movies. Ind Manage Data Syst2015; 115(9): 1604–1621.
2.
ChangW-LWangJ-Y. Mine is yours? Using sentiment analysis to explore the degree of risk in the sharing economy. Electron Commer R A2018; 28: 141–158.
3.
BalahurAHermidaJMMontoyoA. Detecting implicit expressions of sentiment in text based on commonsense knowledge. In: Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis, Portland, OR, 24 June 2011.
4.
PanXHouLLiuK, et al. Do reviews from friends and the crowd affect online consumer posting behaviour differently?Electron Commer R A2018; 29: 102–112.
5.
CasalóLVFlaviánCGuinalíuM. Determinants of the intention to participate in firm-hosted online travel communities and effects on consumer behavioral intentions. Tourism Manage2010; 31(6): 898–911.
TianFWuFChaoK-M, et al. A topic sentence-based instance transfer method for imbalanced sentiment classification of Chinese product reviews. Electron Commer R A2016; 16: 66–76.
8.
WeiSZhengXChenD, et al. A hybrid approach for movie recommendation via tags and ratings. Electron Commer R A2016; 18: 83–94.
9.
García-CumbrerasMÁMontejo-RáezADíaz-GalianoMC. Pessimists and optimists: improving collaborative filtering through sentiment analysis. Expert Syst Appl2013; 40(17): 6758–6765.
10.
FeldmanR. Techniques and applications for sentiment analysis. Commun ACM2013; 56(4): 82–89.
11.
ChoiKYooDKimG, et al. A hybrid online-product recommendation system: combining implicit rating-based collaborative filtering and sequential pattern analysis. Electron Commer R A2012; 11(4): 309–317.
12.
BobadillaJOrtegaFHernandoA, et al. Recommender systems survey. Knowl-Based Syst2013; 46: 109–132.
13.
AdomaviciusGTuzhilinA. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE T Knowl Data En2005; 17(6): 734–749.
14.
LiaoC-LLeeS-J. A clustering based approach to improving the efficiency of collaborative filtering recommendation. Electron Commer R A2016; 18: 1–9.
15.
YangXGuoYLiuY, et al. A survey of collaborative filtering based social recommender systems. Comput Commun2014; 41: 1–10.
16.
MaXLuHGanZ, et al. An explicit trust and distrust clustering based collaborative filtering recommendation approach. Electron Commer R A2017; 25: 29–39.
17.
AkcayolMAUtkuAAydoğanE, et al. A weighted multi-attribute-based recommender system using extended user behavior analysis. Electron Commer R A2018; 28: 86–93.
18.
SunJWangGChengX, et al. Mining affective text to improve social media item recommendation. Inform Process Manag2015; 51(4): 444–457.
19.
LiuHHeJWangT, et al. Combining user preferences and user opinions for accurate recommendation. Electron Commer R A2013; 12(1): 14–23.
LiuB. Sentiment analysis and opinion mining (Synthesis lectures on human language technologies), Vol. 5. San Rafael, CA: Morgan & Claypool Publishers, 2012, pp. 1–167.
22.
HimmatMSalimN. Survey on product review sentiment classification and analysis challenges. In: Proceedings of the 1st international conference on advanced data and information engineering (DaEng-2013), Kuala Lumpur, Malaysia, 16–18 December 2013.
23.
LouD-CYaoT-F. Semantic polarity analysis and opinion mining on Chinese review sentences. J Comput Appl2006; 11: 30–45.
24.
PopescuA-MEtzioniO. Extracting product features and opinions from reviews. In: KaoAPoteetSR (eds) Natural language processing and text mining. London: Springer, 2007, pp. 9–28.
25.
HuMLiuB. Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, WA, 22–25 August 2004.
26.
KuL-WLiangY-TChenH-H. Opinion extraction, summarization and tracking in news and blog corpora. In: Proceedings of the AAAI symposium on computational approaches to analysing weblogs (AAAI-CAAW) January 2006. Stanford, CA: Stanford University.
27.
MoghaddamSEsterM. Opinion digger: an unsupervised opinion miner from unstructured product reviews. In: Proceedings of the 19th ACM international conference on information and knowledge management, Toronto, ON, Canada, 26–30 October 2010.
28.
ZhuangLJingFZhuX-Y. Movie review mining and summarization. In: Proceedings of the 15th ACM international conference on information and knowledge management, Arlington, VA, 6–11 November 2006.
29.
ZhangCZengDLiJ, et al. Sentiment analysis of Chinese documents: from sentence to document level. J Am Soc Inf Sci Tec2009; 60(12): 2474–2487.
30.
ChengL-CHuangC-L. Mining some important features from IMDb reviews. In: Proceedings of the international conference on innovation and management, Singapore, 3–6 February 2015.
31.
AgarwalVBharadwajK. A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity. Soc Netw Anal Min2013; 3(3): 359–379.
32.
CaiYLeungH-FLiQ, et al. Typicality-based collaborative filtering recommendation. IEEE T Knowl Data En2014; 26(3): 766–779.
33.
LekakosGCaravelasP. A hybrid approach for movie recommendation. Multimed Tools Appl2008; 36(1–2): 55–70.
34.
KamaharaJAsakawaTShimojoS, et al. A community-based recommendation system to reveal unexpected interests. In: Proceedings of the 11th international multimedia modelling conference (MMM’2005), Melbourne, VIC, Australia, 12–14 January 2005.
35.
KimH-NJiA-THaI, et al. Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electron Commer R A2010; 9(1): 73–83.
36.
ChengL-CWangH-A. A fuzzy recommender system based on the integration of subjective preferences and objective information. Appl Soft Comput2014; 18: 290–301.
37.
JeongBLeeJChoH. An iterative semi-explicit rating method for building collaborative recommender systems. Expert Syst Appl2009; 36(3): 6181–6186.
38.
LiuD-RLaiC-HLeeW-J. A hybrid of sequential rules and collaborative filtering for product recommendation. Inform Sciences2009; 179(20): 3505–3519.
39.
ChenY-LChengL-CChuangC-N. A group recommendation system with consideration of interactions among group members. Expert Syst Appl2008; 34(3): 2082–2090.
40.
ChuaAYKBanerjeeS. Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Comput Hum Behav2016; 54: 547–554.
41.
YanZXingMZhangD, et al. EXPRS: an extended pagerank method for product feature extraction from online consumer reviews. Inform Manage2015; 52(7): 850–858.
42.
ChengL-CLinM-C. Combined with comments mining of recommendation system. In: Proceedings of the international conference on information management, Taichung, Taiwan, May2016.
43.
ChengL-CTsaoS-H. A novel recommender system to integrate the reviews of products. In: Proceedings of the international conference on the development and application of enterprise resource management, Kaohsiung, March2014.
44.
LiuBZhangL. A survey of opinion mining and sentiment analysis. In: AggarwalCZhaiC (eds) Mining text data. Boston, MA: Springer, 2012, pp. 415–463.
45.
KimH-NHaILeeK-S, et al. Collaborative user modeling for enhanced content filtering in recommender systems. Decis Support Syst2011; 51(4): 772–781.
