Aspect extraction or opinion target extraction is the key task of sentiment analysis, which aims to identify targets of people’s sentiments. This is the most important task of aspect-based sentiment analysis as without the aspects, there is no much use of extracted opinions. Recent approaches have shown the significance of dependency-based rules for the given task. These rules are heavily dependent on the dependency parser and generated with the help of grammatical rules. In this article, we are proposing to learn from user’s behaviour to identify the relation among aspects and opinions. The use of sequential patterns has been proposed for the extraction of aspects. The key purpose of this research is to study the impact of sequential pattern mining in the phase of aspect extraction. Our experimental results show that the approach proposed in our work produced better results as compared with the state-of-the-art approaches.
CambriaE.Affective computing and sentiment analysis. IEEE Intell Syst2016; 31: 102–107.
2.
LiuB.Sentiment analysis and opinion mining. Synth Lect Human Lang Technol2012; 5: 1–167.
3.
PangBLeeL.Opinion mining and sentiment analysis. Found Trends Inf Ret2008; 2: 1–135.
4.
RanaTACheahY-N.Aspect extraction in sentiment analysis: comparative analysis and survey. Artif Intell Rev2016; 46: 459–483.
5.
RanaTACheahY-NLetchmunanS.Topic modeling in sentiment analysis: a systematic review. J ICT Res Appl2016; 10: 76–93.
6.
SchoutenKFrasincarF.Survey on aspect-level sentiment analysis. IEEE Trans Knowl Data Eng2016; 28: 813–830.
7.
HuMLiuB.Mining opinion features in customer reviews. In: Proceedings of the 19th national conference on artificial intelligence (AAAI’04), San Jose, CA, 25–29 July 2004, pp. 755–760. Palo Alto, CA: AAAI Press.
8.
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, pp. 168–177. New York: ACM Press.
9.
BanckenWAlfaroneDDavisJ.Automatically detecting and rating product aspects from textual customer reviews. In: Proceedings of the 1st international workshop on interactions between data mining and natural language processing at ECML/PKDD, Nancy, France, 15 September 2014, pp. 1–16.
10.
KangYZhouL.RubE: rule-based methods for extracting product features from online consumer reviews. Inform Manage2016; 54: 166–176.
11.
LiuQGaoZLiuB, et al. Automated rule selection for opinion target extraction. Knowl Based Syst2016; 104: 74–88.
12.
LiuQGaoZLiuB, et al. Automated rule selection for aspect extraction in opinion mining. In: International joint conference on artificial intelligence (IJCAI), Buenos Aires, Argentina, 25–31 July 2015, pp. 1291–1297. Palo Alto, CA: AAAI Press.
13.
LiuQLiuBZhangY, et al. Improving opinion aspect extraction using semantic similarity and aspect associations. In: 30th AAAI conference on artificial intelligence, Phoenix, AZ, 12–17 February 2016, pp. 2986–2992. Palo Alto, CA: AAAI Press.
14.
PoriaSCambriaEKuL-W, et al. A rule-based approach to aspect extraction from product reviews. In: Proceedings of the second workshop on natural language processing for social media (SocialNLP), Dublin, Ireland, 24 August 2014, pp. 28–37. Stroudsburg, PA: Association for Computational Linguistics.
15.
PeiJHanJMortazavi-AslB, et al. Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE Trans Knowl Data Eng2004; 16: 1424–1440.
16.
BafnaKToshniwalD.Feature based summarization of customers’ reviews of online products. Procedia Comput Sci2013; 22: 142–151.
17.
PopescuA-MEtzioniO.Extracting product features and opinions from reviews. In: KaoAPoteetSR (eds) Natural language processing and text mining. Cham: Springer, pp. 9–28.
18.
AlrababahSAAGanKHTanT-P.Mining opinionated product features using WordNet lexicographer files. J Inf Sci2017; 43: 769–785.
19.
LiZZhangMMaS, et al. Automatic extraction for product feature words from comments on the web. In: Proceedings of the 5th Asia information retrieval symposium on Information retrieval technology, Sapporo, Japan, 21–23 October 2009, pp. 112–123. Berlin: Springer.
20.
RajuSPingaliPVarmaV.An unsupervised approach to product attribute extraction. In: Advances in information retrieval, Toulouse, 6–9 April 2009, pp. 796–800. Berlin: Springer.
21.
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, pp. 1825–1828. New York: ACM Press.
22.
MengXWangH.Mining user reviews: from specification to summarization. In: Proceedings of the ACL-IJCNLP 2009 conference short papers, Singapore, 4 August 2009, pp. 177–180. Stroudsburg, PA: Association for Computational Linguistics.
Marrese-TaylorEVelásquezJDBravo-MarquezF.Opinion zoom: a modular tool to explore tourism opinions on the web. In: IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT), Atlanta, USA, 17–20 November 2013, pp. 261–264. New York: IEEE.
25.
BagheriASaraeeMDe JongF.An unsupervised aspect detection model for sentiment analysis of reviews. In: HopfeCRezguiYMétaisE, et al. (eds) Natural language processing and information systems. Berlin: Springer, 2013, pp. 140–151.
26.
LiYQinZXuW, et al. A holistic model of mining product aspects and associated sentiments from online reviews. Multimed Tools Appl2015; 74: 10177–10194.
27.
ZhuJWangHZhuM, et al. Aspect-based opinion polling from customer reviews. IEEE Trans Affect Comput2011; 2: 37–49.
28.
PontikiMGalanisDPapageorgiouH, et al. SemEval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the semantic evaluation (SemEval-2016), San Diego, CA, 16–17 June 2016, pp. 19–30. Stroudsburg, PA: Association for Computational Linguistics.
29.
RanaTACheahY-N.A two-fold rule-based model for aspect extraction. Expert Syst Appl2017; 89: 273–285.
30.
WuCWuFWuS, et al. A hybrid unsupervised method for aspect term and opinion target extraction. Knowl Based Syst2018; 148: 66–73.
31.
JinWHoHH.A novel lexicalized HMM-based learning framework for web opinion mining. In: Proceedings of the 26th annual international conference on machine learning, Montreal, QC, Canada, 14–18 June 2009, pp. 465–472. New York: ACM Press.
32.
ChenLQiLWangF.Comparison of feature-level learning methods for mining online consumer reviews. Expert Syst Appl2012; 39: 9588–9601.
33.
ChoiYCardieC.Hierarchical sequential learning for extracting opinions and their attributes. In: Proceedings of the ACL 2010 conference short papers. Uppsala, 11–16 July 2010, pp. 269–274. Stroudsburg, PA: Association for Computational Linguistics.
34.
HuangSLiuXPengX, et al. Fine-grained product features extraction and categorization in reviews opinion mining. In: Proceedings of the 12th international conference on data mining workshops (ICDMW), Brussels, Belgium, 10 December 2012, pp. 680–686. New York: IEEE.
35.
JakobNGurevychI.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, Cambridge, MA, 9–11 October, pp. 1035–1045. Stroudsburg, PA: Association for Computational Linguistics.
36.
LiFHanCHuangM, et al. Structure-aware review mining and summarization. In: Proceedings of the 23rd international conference on computational linguistics, Beijing, China, 23–27 2010, pp. 653–661. Stroudsburg, PA: Association for Computational Linguistics.
37.
YangBCardieC.Joint inference for fine-grained opinion extraction. In: Proceedings of the 51st annual meeting of the association for computational linguistics, Sofia, 4–9 August 2013, pp. 1640–1649. Stroudsburg, PA: Association for Computational Linguistics.
38.
LiSWangRZhouG.Opinion target extraction using a shallow semantic parsing framework. In: Proceedings of the 26th AAAI conference on artificial intelligence, Toronto, ON, Canada, 22–26 July 2012, pp. 1671–1677. Palo Alto, CA: AAAI Press.
39.
CruzFLTroyanoJAEnríquezF, et al. ‘Long autonomy or long delay?’ The importance of domain in opinion mining. Expert Syst Appl2013; 40: 3174–3184.
40.
KobayashiNInuiKMatsumotoY.Extracting aspect-evaluation and aspect-of relations in opinion mining. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), Prague, Czech Republic, 28–30 June 2007, pp. 1065–1074. Stroudsburg, PA: Association for Computational Linguistics.
41.
PoriaSCambriaEGelbukhA.Aspect extraction for opinion mining with a deep convolutional neural network. Knowl Based Syst2016; 108: 42–49.
42.
QiuGLiuBBuJ, et al. Opinion word expansion and target extraction through double propagation. Comput Linguist2011; 37: 9–27.
43.
QiuGLiuBBuJ, et al. Expanding domain sentiment lexicon through double propagation. In: Proceedings of the 21st international joint conference on artificial intelligence(IJCAI), Pasadena, CA, 11–17 July 2009, pp. 1199–1204. San Francisco, CA: Morgan Kaufmann Publishers.
44.
ZhangLLiuBLimSH, et al. Extracting and ranking product features in opinion documents. In: Proceedings of the 23rd international conference on computational linguistics: posters, Beijing, China, 23–27 August 2010, pp. 1462–1470. Stroudsburg, PA: Association for Computational Linguistics.
45.
ShamsMBaraani-DastjerdiA.Enriched LDA (ELDA): combination of latent Dirichlet allocation with word co-occurrence analysis for aspect extraction. Expert Syst Appl2017; 80: 136–146.
46.
MaBZhangDYanZ, et al. An LDA and synonym lexicon based approach to product feature extraction from online consumer product reviews. J Electron Commer Res2013; 14: 304–314.
WeiC-PChenY-MYangC-S, et al. Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews. Inf Syst E-Bus Manag2010; 8: 149–167.
49.
YanZXingMZhangD, et al. EXPRS: an extended pagerank method for product feature extraction from online consumer reviews. Inform Manage2015; 52: 850–858.
50.
BagheriASaraeeMDe JongF.ADM-LDA: an aspect detection model based on topic modelling using the structure of review sentences. J Inf Sci2014; 40: 621–636.
51.
BrodySElhadadN.An unsupervised aspect-sentiment model for online reviews. In: Human language technologies: the 2010 annual conference of the North American chapter of the association for computational linguistics, Los Angeles, CA, 2–4 June 2010, pp. 804–812. Stroudsburg, PA: Association for Computational Linguistics.
52.
ChenZLiuB.Topic modeling using topics from many domains, lifelong learning and big data. In: Proceedings of the 31st international conference on machine learning (ICML-14), Beijing, China, 21–26 June 2014, pp. 703–711. JMLR: W&CP.
53.
ChenZLiuB.Mining topics in documents: standing on the shoulders of big data. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, New York, 24–27 August 2014, pp. 1116–1125. New York: ACM Press.
54.
ChenZMukherjeeALiuB.Aspect extraction with automated prior knowledge learning In: Proceedings of the 52nd annual meeting of the association for computational linguistics, Baltimore, MD, 23–25 June 2014, pp. 347–358. Stroudsburg, PA: Association for Computational Linguistics.
55.
ChenZMukherjeeALiuB, et al. Leveraging multi-domain prior knowledge in topic models. In: Proceedings of the 23rd international joint conference on artificial intelligence, Beijing, China, 3–9 August 2013, pp. 2071–2077. Palo Alto, CA: AAAI Press.
56.
ChenZMukherjeeALiuB, et al. Discovering coherent topics using general knowledge. In: Proceedings of the 22nd ACM international conference on information and knowledge management, San Francisco, CA, 27 October–1 November 2013, pp. 209–218. New York: ACM.
57.
ChenZMukherjeeALiuB, et al. Exploiting domain knowledge in aspect extraction. In: Proceedings of the 2013 conference on empirical methods in natural language processing (EMNLP), Seattle, WA, 18–21 October 2013, pp. 1655–1667. Stroudsburg, PA: Association for Computational Linguistics.
58.
FangLHuangM.Fine granular aspect analysis using latent structural models. In: Proceedings of the 50th annual meeting of the association for computational linguistics: short papers, Jeju City, South Korea, 8–14 July 2012, vol. 2, pp. 333–337. Stroudsburg, PA: Association for Computational Linguistics.
59.
LinZJinXXuX, et al. A cross-lingual joint aspect/sentiment model for sentiment analysis. In: Proceedings of the 23rd ACM international conference on information and knowledge management, Shanghai, China, 3–7 November 2014, pp. 1089–1098. New York: ACM Press.
60.
WangTCaiYLeungH, et al. Product aspect extraction supervised with online domain knowledge. Knowl Based Syst2014; 71: 86–100.
61.
ZhaoWXJiangJYanH, et al. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: Proceedings of the 2010 conference on empirical methods in natural language processing, Cambridge, MA, 9–11 October 2010, pp. 56–65. Stroudsburg, PA: Association for Computational Linguistics.
62.
WuYZhangQHuangX, et al. Phrase dependency parsing for opinion mining. In: Proceedings of the 2009 conference on empirical methods in natural language processing, Singapore, 6–7 August 2009, vol. 3, pp. 1533–1541. Stroudsburg, PA: Association for Computational Linguistics.
63.
YuJZhaZ-JWangM, et al. Aspect ranking: identifying important product aspects from online consumer reviews. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, Portland, OR, 19–24 June 2011, vol. 1, pp. 1496–1505. Stroudsburg, PA: Association for Computational Linguistics.
64.
LiuKXuLLiuY, et al. Opinion target extraction using partially-supervised word alignment model. In: Proceedings of the 23rd international joint conference on artificial intelligence, Beijing, China, 3–9 August 2013, pp. 2134–2140. Palo Alto, CA: AAAI Press.
65.
XuLLiuKLaiS, et al. Walk and learn: a two-stage approach for opinion words and opinion targets co-extraction. In: Proceedings of the 22nd international conference on World Wide Web companion, Rio de Janeiro, Brazil, 13–17 May 2013, pp. 95–96. Geneva: International World Wide Web Conferences Steering Committee.
66.
LiuKXuLZhaoJ.Opinion target extraction using word-based translation model. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, Jeju City, South Korea, 12–14 July 2012, pp. 1346–1356. Stroudsburg, PA: Association for Computational Linguistics.
67.
HtaySSLynnKT.Extracting product features and opinion words using pattern knowledge in customer reviews. Sci World J2013; 2013: 394758.
68.
DuJChanWZhouX.A product aspects identification method by using translation-based language model. In: Proceedings of the 22nd international conference on pattern recognition (ICPR), Stockholm, 24–28 August 2014, pp. 2790–2795. New York: IEEE.
69.
PoriaSOfekNGelbukhA, et al. Dependency tree-based rules for concept-level aspect-based sentiment analysis. In: Semantic web evaluation challenge, Crete, 25–29 May 2014, pp. 41–47. Cham: Springer.
70.
HaiZChangKKimJ-J, et al. Identifying features in opinion mining via intrinsic and extrinsic domain relevance. IEEE Trans Knowl Data Eng2014; 26: 623–634.
71.
RanaTACheahY-N.Hybrid rule-based approach for aspect extraction and categorization from customer reviews. In: Proceedings of the 9th international conference on IT in Asia (CITA), Kota Samarahan, Malaysia, 4–5 August 2015, pp. 1–5. New York: IEEE.
72.
RanaTACheahY-N.Sequential patterns-based rules for aspect-based sentiment analysis. In: Proceedings of the 3rd international conference on computational science and technology (ICCST), Kota Kinabalu, Malaysia, 28–30 November 2016.
73.
RanaTACheahY-N.Improving aspect extraction using aspect frequency and semantic similarity-based approach for aspect-based sentiment analysis. In: Recent advances in information and communication technology, Bangkok, Thailand, 6–7 July 2018, pp. 317–326. Cham: Springer.
74.
RanaTACheahY-N.Exploiting sequential patterns to detect objective aspects from online reviews. In: Proceedings of the 3rd international conference on advanced informatics: concepts, theory and application (ICAICTA), George Town, Malaysia, 16–19 August 2016, pp. 1–5. New York: IEEE.
75.
Fournier-VigerPGomarizAGuenicheT, et al. SPMF: a java open-source pattern mining library. J Mach Learn Res2014; 15: 3389–3393.
