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CBVoSD: context based vectors over sentiment domain ensemble model for review classification


With the exponential growth of e-commerce, billions of consumers can share their opinion in chat groups, retailer forums, private forums, or a review page on all kinds of products. Consequently, there is a significant rise in the number of online product reviews on e-commerce websites. However, buyers know less about the products or the seller’s credibility. Therefore, experienced buyers refer to historical reviews that can determine their purchasing decisions. The present research challenges are due to textual anomalies and sentiment expression variations. There are various approaches for evaluating sentiments, but word embedding strategies word2vec and GloVe transform words into meaningful vectors. However, these approaches neglect the word’s sentiment information. To generate an appropriate word vector based on sentiment analysis context, we proposed the context-based vectors over sentiment domain(CBVoSD) algorithm with ensemble model to collect the sentiment information, which improves sentiment classification accuracy. Due to their ever-changing dynamics, the suggested method identifies and categorizes sentiment toward products and services. The experiment results show that the CBVoSD ensemble method improves sentiment classification compared to state-of-the-art methods.

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  1. 1.


  1. 1.

    Weijun H, Hui W, Ling L, Ramsey TS, Zhengwei H (2020) Study of e-smile service influence on customers’ satisfaction in social business context. J Supercomput 76(5):3673–3688

  2. 2.

    Hailong Z, Wenyan G, Bo J (2014) Machine learning and lexicon based methods for sentiment classification: a survey. In: 2014 11th Web Information System and Application Conference. IEEE, pp 262–265

  3. 3.

    Liu B, Zhang L (2012) A survey of opinion mining and sentiment analysis. In: Mining text data. Springer, pp 415–463

  4. 4.

    Chikersal P, Poria S, Cambria E (2015) Sentu: sentiment analysis of tweets by combining a rule-based classifier with supervised learning. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), pp 647–651

  5. 5.

    Jurek A, Mulvenna MD, Bi Y (2015) Improved lexicon-based sentiment analysis for social media analytics. Sec Inform 4(1):1–13

    Article  Google Scholar 

  6. 6.

    Giatsoglou M, Vozalis MG, Diamantaras K, Vakali A, Sarigiannidis G, Chatzisavvas KC (2017) Sentiment analysis leveraging emotions and word embeddings. Exp Syst Appl 69:214–224

    Article  Google Scholar 

  7. 7.

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

    Article  Google Scholar 

  8. 8.

    Lu Y, Castellanos M, Dayal U, Zhai C (2011) Automatic construction of a context-aware sentiment lexicon: an optimization approach. In: Proceedings of the 20th International Conference on World Wide Web, pp 347–356

  9. 9.

    Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307

    Article  Google Scholar 

  10. 10.

    Goudjil M, Koudil M, Bedda M, Ghoggali N (2018) A novel active learning method using svm for text classification. Int J Autom Comput 15(3):290–298

    Article  Google Scholar 

  11. 11.

    Camacho-Collados J, Pilehvar MT, Navigli R (2016) Nasari: integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities. Artif Intell 240:36–64

    MathSciNet  Article  Google Scholar 

  12. 12.

    Wang Y (2020) Iteration-based naive bayes sentiment classification of microblog multimedia posts considering emoticon attributes. Multim Tools Appl 1–16

  13. 13.

    Bordoloi M, Biswas SK (2020) Graph based sentiment analysis using keyword rank based polarity assignment. Multim Tools Appl 1–30

  14. 14.

    Araque O, Corcuera-Platas I, Sánchez-Rada JF, Iglesias CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Exp Syst Appl 77:236–246

    Article  Google Scholar 

  15. 15.

    Rong X (2014) word2vec parameter learning explained. arXiv preprint arXiv:14112738

  16. 16.

    Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1532–1543

  17. 17.

    Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp 606–615

  18. 18.

    Tripathy A, Agrawal A, Rath SK (2016) Classification of sentiment reviews using n-gram machine learning approach. Exp Syst Appl 57:117–126

    Article  Google Scholar 

  19. 19.

    Salvetti F, Lewis S, Reichenbach C (2004) Automatic opinion polarity classification of movie reviews. Colorado Res Linguist 17

  20. 20.

    Matsumoto S, Takamura H, Okumura M (2005) Sentiment classification using word sub-sequences and dependency sub-trees. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, pp 301–311

  21. 21.

    Dey A, Jenamani M, Thakkar JJ (2018) Senti-n-gram: an n-gram lexicon for sentiment analysis. Exp Syst Appl 103:92–105

    Article  Google Scholar 

  22. 22.

    Liu SM, Chen JH (2015) A multi-label classification based approach for sentiment classification. Exp Syst Appl 42(3):1083–1093

    Article  Google Scholar 

  23. 23.

    Luo B, Zeng J, Duan J (2016) Emotion space model for classifying opinions in stock message board. Exp Syst Appl 44:138–146

    Article  Google Scholar 

  24. 24.

    Canales L, Martinez-Barco P (2014) Emotion detection from text: a survey. In: Proceedings of the workshop on natural language processing in the 5th information systems research working days (JISIC), pp 37–43

  25. 25.

    Priya K, Dinakaran K, Valarmathie P (2020) Multilevel sentiment analysis using domain thesaurus. J Amb Intell Human Comput

  26. 26.

    Rezaeinia SM, Rahmani R, Ghodsi A, Veisi H (2019) Sentiment analysis based on improved pre-trained word embeddings. Exp Syst Appl 117:139–147

    Article  Google Scholar 

  27. 27.

    Leng J, Ruan G, Song Y, Liu Q, Fu Y, Ding K, Chen X (2021) A loosely-coupled deep reinforcement learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in industry 4.0. J Clean prod 280:124405

    Article  Google Scholar 

  28. 28.

    Chao CT, Chu WH, Lee CL, Lee JK, Hung MY, Sung HW (2020) Devise sparse compression schedulers to enhance fasttext methods. In: 49th International Conference on Parallel Processing-ICPP: Workshops, pp 1–8

  29. 29.

    Joulin A, Grave E, Bojanowski P, Douze M, Jégou H, Mikolov T (2016) Fasttext. zip: compressing text classification models. arXiv preprint arXiv:1612.03651

  30. 30.

    Hossain M, Hoque MM, Sarker IH, et al. (2020) Text classification using convolution neural networks with fasttext embedding. In: International Conference on Hybrid Intelligent Systems. Springer, pp 103–113

  31. 31.

    Hutto CJ, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media

  32. 32.

    Zhang Y, Roller S, Wallace B (2016) Mgnc-cnn: A simple approach to exploiting multiple word embeddings for sentence classification. arXiv preprint arXiv:1603.00968

  33. 33.

    Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820

  34. 34.

    Caliskan A, Bryson JJ, Narayanan A (2017) Semantics derived automatically from language corpora contain human-like biases. Science 356(6334):183–186

    Article  Google Scholar 

  35. 35.

    Reddy DA, Kumar MA, Soman K (2019) Lstm based paraphrase identification using combined word embedding features. In: Soft computing and signal processing. Springer, pp 385–394

  36. 36.

    Alarifi A, Tolba A, Al-Makhadmeh Z, Said W (2020) A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks. J Supercomput 76(6):4414–4429

    Article  Google Scholar 

  37. 37.

    Mishra S, Banerjee M (2020) Automatic caption generation of retinal diseases with self-trained rnn merge model. In: Advanced computing and systems for security, Springer, pp 1–10

  38. 38.

    Dauphin YN, Pascanu R, Gulcehre C, Cho K, Ganguli S, Bengio Y (2014) Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. In: Advances in neural information processing systems, pp 2933–2941

  39. 39.

    Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882

  40. 40.

    Mandhula T, Pabboju S, Gugulotu N (2019) Predicting the customer’s opinion on amazon products using selective memory architecture-based convolutional neural network. J Supercomput 1–25

  41. 41.

    Balakrishnan V, Lok PY, Rahim HA (2020) A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews. J Supercomput 1–16

  42. 42.

    Yuan Z, Wu S, Wu F, Liu J, Huang Y (2018) Domain attention model for multi-domain sentiment classification. Knowl Based Syst 155:1–10

    Article  Google Scholar 

  43. 43.

    Lee H, Lee N, Seo H, Song M (2020) Developing a supervised learning-based social media business sentiment index. J Supercomput 76(5):3882–3897

    Article  Google Scholar 

  44. 44.

    Jin N, Wu J, Ma X, Yan K, Mo Y (2020) Multi-task learning model based on multi-scale cnn and lstm for sentiment classification. IEEE Access 8:77060–77072

    Article  Google Scholar 

  45. 45.

    Zeng B, Yang H, Liu S, Xu M (2021) Learning for target-dependent sentiment based on local context-aware embedding. J Supercomput 1–19

  46. 46.

    Leng J, Chen Q, Mao N, Jiang P (2018) Combining granular computing technique with deep learning for service planning under social manufacturing contexts. Knowl Based Syst 143:295–306

    Article  Google Scholar 

  47. 47.

    Leng J, Jiang P (2017) Granular computing-based development of service process reference models in social manufacturing contexts. Concurr Eng 25(2):95–107

    Article  Google Scholar 

  48. 48.

    Leng J, Jiang P (2016) Mining and matching relationships from interaction contexts in a social manufacturing paradigm. IEEE Trans Syst Man Cybern Syst 47(2):276–288

    Google Scholar 

  49. 49.

    Puhl RM, Luedicke J (2012) Weight-based victimization among adolescents in the school setting: emotional reactions and coping behaviors. J Youth Adoles 41(1):27–40

    Article  Google Scholar 

  50. 50.

    Verma MK, Dwivedi R, Mallick AK, Jangam E (2018) Dimensionality reduction technique on sift feature vector for content based image retrival. In: International Conference on Recent Trends in Image Processing and Pattern Recognition. Springer, pp 383–394

  51. 51.

    Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B (2014) Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd annual meeting of the association for computational linguistics, vol 1: long papers, pp 1555–1565

  52. 52.

    Carrillo-de Albornoz J, Plaza L (2013) An emotion-based model of negation, intensifiers, and modality for polarity and intensity classification. J Am Soc Inform Sci Technol 64(8):1618–1633

    Article  Google Scholar 

  53. 53.

    Wankhade M, Rao ACS, Dara S, Kaushik B (2017) A sentiment analysis of food review using logistic regression. Int J Sci Res Comput Sci Eng InformTechnol 2–17

  54. 54.

    Khan FH, Qamar U, Bashir S (2017) A semi-supervised approach to sentiment analysis using revised sentiment strength based on sentiwordnet. Knowl Inform Syst 51(3):851–872

    Article  Google Scholar 

  55. 55.

    Kuang S, Davison BD (2020) Learning class-specific word embeddings. J Supercomput 76(10):8265–8292

    Article  Google Scholar 

  56. 56.

    Iyyer M, Enns P, Boyd-Graber J, Resnik P (2014) Political ideology detection using recursive neural networks. In: Proceedings of the 52nd annual meeting of the association for computational linguistics, vol 1: long papers, pp 1113–1122

  57. 57.

    Sohrabi MK, Hemmatian F (2019) An efficient preprocessing method for supervised sentiment analysis by converting sentences to numerical vectors: a twitter case study. Multim Tools Appl 78(17):24863–24882

    Article  Google Scholar 

  58. 58.

    Pylkkanen L (2019) The neural basis of combinatory syntax and semantics. Science 366(6461):62–66

    Article  Google Scholar 

  59. 59.

    Ren Y, Wang R, Ji D (2016) A topic-enhanced word embedding for twitter sentiment classification. Inform Sci 369:188–198

    Article  Google Scholar 

  60. 60.

    Kumar A, Garg G (2019) Sentiment analysis of multimodal twitter data. Multim Tools Appl 78(17):24103–24119

    Article  Google Scholar 

  61. 61.

    Hu K, Wu H, Qi K, Yu J, Yang S, Yu T, Zheng J, Liu B (2018) A domain keyword analysis approach extending term frequency-keyword active index with google word2vec model. Scientometrics 114(3):1031–1068

    Article  Google Scholar 

  62. 62.

    Kumar A, Garg G (2019) Systematic literature review on context-based sentiment analysis in social multimedia. Multim Tools Appl 1–32

  63. 63.

    Maas A, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 142–150

  64. 64.

    Priyadarshini I, Cotton C (2021) A novel lstm–cnn–grid search-based deep neural network for sentiment analysis. J Supercomput 1–22

  65. 65.

    Geethapriya A, Valli S (2021) An enhanced approach to map domain-specific words in cross-domain sentiment analysis. Inform Syst Front 1–15

  66. 66.

    Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 168–177

  67. 67.

    Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th International Conference on World Wide Web, pp 342–351

  68. 68.

    Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078

  69. 69.

    Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with lstm. Neural comput 12(10):2451–2471

    Article  Google Scholar 

  70. 70.

    Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? sentiment classification using machine learning techniques. arXiv preprint arXiv:cs/0205070v1

  71. 71.

    Mudinas A, Zhang D, Levene M (2012) Combining lexicon and learning based approaches for concept-level sentiment analysis. In: Proceedings of the first international workshop on issues of sentiment discovery and opinion mining, pp 1–8

  72. 72.

    Peng Q, Zhong M (2014) Detecting spam review through sentiment analysis. J Softw 9(8):2065–2072

    Article  Google Scholar 

  73. 73.

    Li C, Guo X, Mei Q (2017) Deep memory networks for attitude identification. In: Proceedings of the tenth ACM International Conference on Web Search and Data Mining, pp 671–680

  74. 74.

    Tay Y, Tuan LA, Hui SC (2017) Dyadic memory networks for aspect-based sentiment analysis. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 107–116

  75. 75.

    Baktha K, Tripathy B (2017) Investigation of recurrent neural networks in the field of sentiment analysis. In: 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, pp 2047–2050

  76. 76.

    Shuang K, Zhang Z, Guo H, Loo J (2018) A sentiment information collector-extractor architecture based neural network for sentiment analysis. Inform Sci 467:549–558

    Article  Google Scholar 

  77. 77.

    Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In: Proceedings of the 45th annual meeting of the association of computational linguistics, pp 440–447

  78. 78.

    Zhang Z, Zou Y, Gan C (2018) Textual sentiment analysis via three different attention convolutional neural networks and cross-modality consistent regression. Neurocomputing 275:1407–1415

    Article  Google Scholar 

  79. 79.

    Li B, Cheng Z, Xu Z, Ye W, Lukasiewicz T, Zhang S (2019) Long text analysis using sliced recurrent neural networks with breaking point information enrichment. In: ICASSP 2019–2019 IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP). IEEE, pp 7550–7554

  80. 80.

    Xu F, Pan Z, Xia R (2020) E-commerce product review sentiment classification based on a naïve bayes continuous learning framework. Inform Process Manage 102221

  81. 81.

    Shen J, Ma MD, Xiang R, Lu Q, Vallejos EP, Xu G, Huang CR, Long Y (2020) Dual memory network model for sentiment analysis of review text. Knowl Based Syst 188:105004

    Article  Google Scholar 

  82. 82.

    Alharbi NM, Alghamdi NS, Alkhammash EH, Al Amri JF (2021) Evaluation of sentiment analysis via word embedding and rnn variants for amazon online reviews. Math Probl Eng

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Correspondence to Chandra Sekhara Rao Annavarapu.

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Wankhade, M., Annavarapu, C.S.R. & Verma, M.K. CBVoSD: context based vectors over sentiment domain ensemble model for review classification. J Supercomput (2021).

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  • Sentiment classification
  • Machine learning
  • Word embedding
  • Review analysis