With the rapid development of social media and mobile Internet, short reviews, such as Weibo and Twitter, have exploded online. Discovering topics from short reviews is significant for many practical applications. It can effectively not only identify users’ attitudes and emotions but also enhance customer satisfaction and shopping experience. Because reviews are relatively short, the sparsity of reviews considerably restricts the quality of topic discovery. To improve the efficiency of topic discovery, we introduce the concept of data enhancement and strengthen the data in sentences and words in short reviews based on the weight of importance. We then propose a topic model for reviews to topic discovery based on data enhancement (shorted as DE-LDA). We verify the rationality and feasibility of DE-LDA on real datasets. Results show that the proposed method outperforms benchmarks in topic discovery and also has better clustering effects.
DařenaF. et al., Machine learning-based analysis of the association between online texts and stock price movements, Inteligencia Artificial21(61) (2018), 95–110.
2.
LianY.DongX. and LiuY., Topological evolution of the internet public opinion, Physica A: Statistical Mechanics and its Applications486 (2017), 567–578.
3.
Camacho-VázquezV.A.SidorovG. and Galicia-HaroS.N., Automatic detection of negative emotions within a balanced corpus of informal short texts, Cyberpsychology, Behavior, and Social Networking21(12) (2018), 781–787.
4.
FanZ.CheY. and ChenZ., Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis, Journal of Business Research74 (2017), 90–100.
5.
LiuY.FengJ. and LiaoX., When online reviews meet sales volume information: is more or accurate information always better? Information Systems Research28(4) (2017), 723–743.
6.
HofmannT., Probabilistic latent semantic indexing, in: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1999, pp. 50–57.
7.
WengJ. et al., Twitterrank: finding topic-sensitive influential twitterers, in: Proceedings of the Third ACM International Conference on Web Search and Data Mining, 2010, pp. 261–270.
8.
HongL. and DavisonB.D., Empirical study of topic modeling in twitter, in: Proceedings of the First Workshop on Social Media Analytics, 2010, pp. 80–88.
9.
QuanX. et al., Short and sparse text topic modeling via self-aggregation, in: Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.
10.
ZuoY. et al., Topic modeling of short texts: A pseudo-document view, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 2105–2114.
11.
PhelanO.McCarthyK. and SmythB., Using twitter to recommend real-time topical news, in: Proceedings of the Third ACM Conference on Recommender Systems (2009), 385–388.
12.
GruberA.WeissY. and Rosen-ZviM., Hidden topic markov models, in Artificial Intelligence and Statistics, 2007, 163–170.
13.
AndrzejewskiD.ZhuX. and CravenM., Incorporating domain knowledge into topic modeling via Dirichlet forest priors, in: Proceedings of the 26th Annual International Conference on Machine Learning, 2009, pp. 25–32.
14.
ChengX. et al., Btm: Topic modeling over short texts, IEEE Transactions on Knowledge and Data Engineering26(12) (2014), 2928–2941.
15.
BaykulovM. and GajewskiD., Prestack seismic data enhancement with partial common-reflection-surface (CRS) stack, Geophysics74(3) (2009), 49–58.
16.
WuR. et al., Deep image: Scaling up image recognition, arXiv preprint arXiv:1501.02876, 2015.
17.
WangJ. and PerezL., The effectiveness of data augmentation in image classification using deep learning, in: Convolutional Neural Networks Vis. Recognit, 2017.
18.
WongS.C. et al., Understanding data augmentation for classification: when to warp? in: 2016 International Conference on Digital Image Computing: Techniques and Applications, 2016, pp. 1–6.
19.
XuY. et al., Improved relation classification by deep recurrent neural networks with data augmentation, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2016.
20.
VasconcelosC.N. and VasconcelosB.N., Increasing deep learning melanoma classification by classical and expert knowledge based image transforms, CoRR (2017).
21.
YanX. et al., A biterm topic model for short texts, in: Proceedings of the 22nd International Conference on World Wide Web, 2013, pp. 1445–1456.
22.
LinC. and HeY., Joint sentiment topic model for sentiment analysis, in: Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009, pp. 375–384.
23.
AsuncionA. et al., On smoothing and inference for topic models, in: Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence, 2009, pp. 27–34.
24.
MimnoD. et al., Optimizing semantic coherence in topic models, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011, pp. 262–272.
25.
NigamK. et al., Text classification from labeled and unlabeled documents using EM, Machine Learning39(2-3) (2000), 103–134.
26.
LiuY. et al., A crowdsourcing-based topic model for service matchmaking in Internet of Things, Future Generation Computer Systems87 (2018), 186–197.
27.
HartiganJ.A. and WongM.A., Algorithm AS 136: A K-Means clustering algorithm, Journal of the Royal Statistical Society. Series C (Applied Statistics)28(1) (1979), 100–108.
28.
ZivkovicZ., Improved adaptive gaussian mixture model for background subtraction, in: Proceedings of the 17th International Conference on Pattern Recognition, 2004, pp. 28–31.
29.
RandW.M., Objective criteria for the evaluation of clustering methods, Publications of the American Statistical Association66(336) (1971), 846–850.
30.
BleiD.M.NgA.Y. and JordanM.I., Latent dirichlet allocation, Journal of Machine Learning Research (2003), 993–1022.
31.
FarisP.D. et al., Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses, Journal of Clinical Epidemiology55(2) (2002), 184–191.
32.
WuN.WangR. and TudoreanuM.E., Utilizing public data for data enhancement and analysis of federal acquisition data, 2018.
33.
BaykulovM. and GajewskiD., Prestack seismic data enhancement with partial common-reflection-surface (CRS) stack, Geophysics74(3) (2009), 49–58.
34.
RamageD.DumaisS. and LieblingD., Characterizing microblogs with topic models, Proceedings of the International AAAI Conference on Web and Social Media4(1) (2000).
35.
WangY.AgichteinE. and BenziM., TM-LDA: efficient online modeling of latent topic transitions in social media, in: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012, pp. 123–131.
