Skip to main content
SpringerLink
Log in

A comprehensive analysis of adverb types for mining user sentiments on amazon product reviews

World Wide Web Aims and scope Submit manuscript

Cite this article

Abstract

Online shopping websites like Amazon stipulate a platform to the users where they can share their opinions about different products. Recently, it has been identified that prior to the purchasing, 81% of the users explore different online platforms in order to assess the reliability of product that they intend to buy. The reviews of different users are expressed by using natural language, which help a user to make an informed decision. From past few years, scientific community has payed attention to automatically specify the meaning of review through Sentiment Analysis. Sentiment Analysis is a research area which is gradually being evolved thus, helping the users to tackle the sentiment hidden in a review. To date, different sentiment analysis-based studies have been conducted in literature. For sentiment classification, the core ingredient is the exploitation of polarity bearing words present in the reviews e.g. adjectives, verbs, and adverbs etc. Different studies suggest the importance of different forms of adverbs in sentiment classification task. In literature, it has been reported that general adverbs strongly help to classify sentiments with better accuracy whereas other suggest that degree adverbs are important for sentiment classification. There are ten distinct forms of adverbs such as general adverbs, general superlative adverbs, general comparative adverbs, general-wh adverbs, degree adverbs, degree superlative adverbs, degree comparative adverbs, degree-wh adverbs, time adverbs and locative adverbs. In this paper, we intend to tackle a question that what is the impact of different forms of adverb on the classification of sentiments? For this, the impacts of all these forms have been evaluated on 51,005 reviews of two products, office products and musical DVDs acquired from Amazon. The outcomes of study revealed that two general superlative adverbs and degree-wh adverb hold more impact than the other forms of adverbs. The general superlative adverbs have attained F-measure of 0.86 and degree-wh adverbs have attained F-measure of 0.80.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

References

  1. Alrababah, S.A.A., Gan, K.H., Tan, T.-P.: Mining opinionated product features using WordNet lexicographer files. J. Inform. Sci. 43(6), 769–785 (2017)

    Article  Google Scholar 

  2. Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Lrec (2010)

  3. Benamara, F., et al.: Sentiment analysis: adjectives and adverbs are better than adjectives alone. In: ICWSM. Citeseer (2007)

  4. Bjorkelund, E., Burnett, T.H., Norvag, K.: A study of opinion mining and visualization of hotel reviews. In: Proceedings of the 14th International Conference on Information Integration and Web-based Applications and Services. ACM (2012)

  5. Boiy, E., et al.: Automatic sentiment analysis in on-line text. In: ELPUB (2007)

  6. Chesley, P., et al.: Using verbs and adjectives to automatically classify blog sentiment. Training 580(263), 233 (2006)

    Google Scholar 

  7. Das, O., Balabantaray, R.C.: Sentiment analysis of movie reviews using POS tags and term frequencies. Int. J. Comput. Appl. (IJCA) 96(25), 36–41 (2014)

    Google Scholar 

  8. Dragoni, M., Poria, S., Cambria, E.: OntoSenticNet: a commonsense ontology for sentiment analysis. IEEE Intell. Sys. 33(3) (2018)

    Article  Google Scholar 

  9. Dragut, E., Fellbaum, C.: The role of adverbs in sentiment analysis. In: Proceedings of Frame Semantics in NLP: a Workshop in Honor of Chuck Fillmore (1929-2014) (2014)

  10. Dray, G., Plantié, M., Harb, A., Poncelet, P., Roche, M., Trousset, F.: Opinion mining from blogs. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 1, 205–213 (2009)

    Google Scholar 

  11. Haider, S., Tanvir Afzal, M., Asif, M., Maurer, H., Ahmad, A., Abuarqoub, A.: Impact analysis of adverbs for sentiment classification on Twitter product reviews. Concurrency and Computation: Practice and Experience, e4956 (2018)

  12. Jong, J.: Predicting rating with sentiment analysis. Citeseer (2011)

  13. Kalarani, P., Brunda, S.: Sentiment analysis by POS and joint sentiment topic features using SVM and ANN. Soft. Comput 23(1), 1–13 (2018)

    Google Scholar 

  14. Khan, K., et al.: Mining opinion components from unstructured reviews: a review. Journal of King Saud University-Computer and Information Sciences 26(3), 258–275 (2014)

    Article  Google Scholar 

  15. Kincl, T., Novák, M., Pribil, J.: Getting inside the minds of the customers: automated sentiment analysis. In: European Conference on Management, Leadership and Governance. Academic Conferences International Limited (2013)

  16. K.M, A.K., Suresha: Analyzing Web user’ opinion from phrases and emoticons. International Journal of Computer Applications (IJCA) Special Issue on Computational Science - New Dimensions & Perspectives NCCSE 4, 133–139 (2011)

    Google Scholar 

  17. Lak, P., Turetken, O.: Star ratings versus sentiment analysis–a comparison of explicit and implicit measures of opinions. In: 2014 47th Hawaii International Conference on System Sciences (HICSS). IEEE (2014)

  18. Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)

    Article  Google Scholar 

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

  20. Mai, F., Wang, X.S., Curry, D., Chiang, R.H.L.: Mining product reviews: market structure analysis using deep learning and evolutionary clustering. SSRN Electron. J. https://doi.org/10.2139/ssrn.2724124 (2016)

  21. Moghaddam, S., Popowich, F.: Opinion polarity identification through adjectives. arXiv:1011.4623 (2010)

  22. Morrison, K.: 81% of shoppers conduct online research before buying AdWeek (2014)

  23. Mudinas, A., Zhang, D., Levene, M.: 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. ACM (2012)

  24. Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Semantically distinct verb classes involved in sentiment analysis. In: IADIS AC (1) (2009)

  25. Padmaja, S., Fatima, S.S., Bandu, S.: Evaluating sentiment analysis methods and identifying scope of negation in newspaper articles. Int. J. Adv. Res. Artificial Intell. 3(11) (2014)

  26. Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics (2005)

  27. Pappas, N., Popescu-Belis, A.: Explaining the stars weighted multiple-instance learning for aspect-based sentiment analysis. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)

  28. Rayson, P., Garside, R.: The claws Web tagger. ICAME J. 22, 121–123 (1998)

    Google Scholar 

  29. Rill, S., et al.: A generic approach to generate opinion lists of phrases for opinion mining applications. In: Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining. ACM (2012)

  30. Rothe, S., Ebert, S., Schütze, H.: Ultradense word embeddings by orthogonal transformation. arXiv:1602.07572 (2016)

  31. Rout, J.K., et al.: A model for sentiment and emotion analysis of unstructured social media text. Electron. Commer. Res. 18(1), 181–199 (2018)

    Article  Google Scholar 

  32. Shaw, R., et al.: Building a scalable database-driven reverse dictionary. IEEE Trans. Knowl. Data Eng. 25(3), 528–540 (2013)

    Article  Google Scholar 

  33. Somprasertsri, G., Lalitrojwong, P.: Mining feature-opinion in online customer reviews for opinion summarization. J. UCS 16(6), 938–955 (2010)

    Google Scholar 

  34. Soni, V., Patel, M.R.: Unsupervised opinion mining from text reviews using sentiwordnet. Int. J. Comput. Trends Technol (IJCTT) 11, 0 (2014)

    Google Scholar 

  35. Thomson, A.J., Martinet, A.V., Draycott, E.: A practical English grammar (1986)

  36. Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, pp 417–424 (2002)

  37. Vinodhini, G., Chandrasekaran, R.: Sentiment analysis and opinion mining: a survey. Int. J. 2(6), 282–292 (2012)

    Google Scholar 

  38. Wang, H., et al.: Feature-based sentiment analysis approach for product reviews. J. Softw. 9(2), 274–279 (2014)

    Google Scholar 

  39. Zafar, L., Ahmed, I., Aleem, M., Islam, M.A., Iqbal, M.A.: Analyzing adverbs impact for sentiment analysis using hadoop. In: 2017 13th International Conference on Emerging Technologies (ICET). IEEE (2017)

  40. Zhang, K., Narayanan, R., Choudhary, A.N.: Voice of the customers: mining online customer reviews for product feature-based ranking. WOSN 10, 11–11 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan

    Ummara Ahmed Chauhan & Muhammad Tanvir Afzal

  2. Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan

    Abdul Shahid

  3. Department of Computer Science, University of Quebec in Montreal, Montreal, Quebec, Canada

    Moloud Abdar

  4. Department of Computer Engineering, Shahrekord University, Shahrekord, Iran

    Mohammad Ehsan Basiri

  5. School of Management and Enterprise, University of Southern Queensland, Queensland, Australia

    Xujuan Zhou

Authors
  1. Ummara Ahmed Chauhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Tanvir Afzal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdul Shahid
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Moloud Abdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Ehsan Basiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xujuan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xujuan Zhou.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article belongs to the Topical Collection: Computational Social Science as the Ultimate Web Intelligence

Guest Editors: Xiaohui Tao, Juan D. Velasquez, Jiming Liu, and Ning Zhong

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chauhan, U.A., Afzal, M.T., Shahid, A. et al. A comprehensive analysis of adverb types for mining user sentiments on amazon product reviews. World Wide Web 23, 1811–1829 (2020). https://doi.org/10.1007/s11280-020-00785-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-020-00785-z

Keywords

search

Navigation