Skip to main content
SpringerLink
Log in

PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis

  • Conference paper
  • First Online:
Computational Data and Social Networks (CSoNet 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11917))

Included in the following conference series:

Abstract

With the current upsurge in the usage of social media platforms, the trend of using short text (microtext) in place of text with standard words has seen a significant rise. The usage of microtext poses a considerable performance issue to sentiment analysis, since models are trained on standard words. This paper discusses the impact of coupling sub-symbolic (phonetics) with symbolic (machine learning) Artificial Intelligence to transform the out-of-vocabulary (OOV) concepts into their standard in-vocabulary (IV) form. We develop binary classifier to detect OOV sentences and then they are transformed to phoneme subspace using grapheme to phoneme converter. We compare the phonetic and string distance using the Sorensen similarity algorithm. The phonetically similar IV concepts thus obtained are then used to compute the correct polarity value, which was previously being miscalculated because of the presence of microtext. Our proposed framework improves the accuracy of polarity detection by 6% as compared to the earlier model. In conclusion, we apply a grapheme to phoneme converter for microtext normalization and show its application on sentiment analysis.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.internationalphoneticassociation.org/content/full-ipa-chart.

  2. 2.

    http://sentic.net/senticnet-5.0.zip.

  3. 3.

    http://github.com/kite1988/nus-sms-corpus.

  4. 4.

    Repetition of a soundex encoding for greater than one.

  5. 5.

    https://sentic.net/demos/#polarity.

References

  1. Aw, A., Zhang, M., Xiao, J., Su, J.: A phrase-based statistical model for SMS text normalization. In: ACL, pp. 33–40 (2006)

    Google Scholar 

  2. Beaufort, R., Roekhaut, S., Cougnon, L.A.l., Fairon, C.d.: A hybrid rule/model-based finite-state framework for normalizing SMS messages. In: ACL, pp. 770–779. Association for Computational Linguistics (2010)

    Google Scholar 

  3. Bisani, M., Ney, H.: Joint-sequence models for grapheme-to-phoneme conversion. Speech Commun. 50(5), 434–451 (2008)

    Article  Google Scholar 

  4. Brill, E., Moore, R.C.: An improved error model for noisy channel spelling correction. In: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, pp. 286–293 (2000)

    Google Scholar 

  5. Brown, A.: Singapore English in a Nutshell: An Alphabetical Description of its Features. Federal Publications, Singapore (1999)

    Google Scholar 

  6. Cambria, E., Benson, T., Eckl, C., Hussain, A.: Sentic PROMs: application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Syst. Appl. 39(12), 10533–10543 (2012)

    Article  Google Scholar 

  7. Cambria, E., Hussain, A., Durrani, T., Havasi, C., Eckl, C., Munro, J.: Sentic computing for patient centered applications. In: IEEE ICSP, pp. 1279–1282 (2010)

    Google Scholar 

  8. Cambria, E., Hussain, A., Havasi, C., Eckl, C.: Sentic computing: exploitation of common sense for the development of emotion-sensitive systems. In: Esposito, A., Campbell, N., Vogel, C., Hussain, A., Nijholt, A. (eds.) Development of Multimodal Interfaces: Active Listening and Synchrony. LNCS, vol. 5967, pp. 148–156. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12397-9_12

    Chapter  Google Scholar 

  9. Cambria, E., Poria, S., Gelbukh, A., Thelwall, M.: Sentiment analysis is a big suitcase. IEEE Intell. Syst. 32(6), 74–80 (2017)

    Article  Google Scholar 

  10. Cambria, E., Poria, S., Hazarika, D., Kwok, K.: SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Thirty-Second AAAI Conference on Artificial Intelligence, pp. 1795–1802 (2018)

    Google Scholar 

  11. Choudhury, M., Saraf, R., Jain, V., Sarkar, S., Basu, A.: Investigation and modeling of the structure of texting language. Int. J. Doc. Anal. Recogn. 10(3–4), 157–174 (2007)

    Article  Google Scholar 

  12. Church, K.W., Gale, W.A.: Probability scoring for spelling correction. Stat. Comput. 1(2), 93–103 (1991)

    Article  Google Scholar 

  13. Cook, P., Stevenson, S.: An unsupervised model for text message normalization. In: Proceedings of the Workshop on Computational Approaches to Linguistic Creativity, pp. 71–78 (2009)

    Google Scholar 

  14. Han, B., Baldwin, T.: Lexical normalisation of short text messages: Makn sens a# Twitter. In: ACL, pp. 368–378 (2011)

    Google Scholar 

  15. Howard, N., Cambria, E.: Intention awareness: improving upon situation awareness in human-centric environments. Human-centric Comput. Inf. Sci. 3(9), 1–17 (2013)

    Google Scholar 

  16. Hutto, C.J., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media, pp. 216–225 (2014)

    Google Scholar 

  17. Kaufmann, M., Kalita, J.: Syntactic normalization of Twitter messages. natural language processing, Kharagpur, India (2010)

    Google Scholar 

  18. Khoury, R.: Microtext normalization using probably-phonetically-similar word discovery. In: 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 392–399 (2015)

    Google Scholar 

  19. Kobus, C., Yvon, F., Damnati, G.é.: Normalizing SMS: are two metaphors better than one? In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 441–448. Association for Computational Linguistics (2008)

    Google Scholar 

  20. Laurent, A., Deléglise, P., Meignier, S.: Grapheme to phoneme conversion using an SMT system. In: Tenth Annual Conference of the International Speech Communication Association, pp. 708–711 (2009)

    Google Scholar 

  21. Li, M., Zhang, Y., Zhu, M., Zhou, M.: Exploring distributional similarity based models for query spelling correction. In: ACL, pp. 1025–1032 (2006)

    Google Scholar 

  22. Li, Z., Yarowsky, D.: Unsupervised translation induction for Chinese abbreviations using monolingual corpora. In: Proceedings of ACL-08: HLT, pp. 425–433 (2008)

    Google Scholar 

  23. Liu, F., Weng, F., Wang, B., Liu, Y.: Insertion, deletion, or substitution? normalizing text messages without pre-categorization nor supervision. ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies 2, pp. 71–76 (2011)

    Google Scholar 

  24. Mortensen, D.R., Dalmia, S., Littell, P.: Epitran: precision G2P for many languages. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), pp. 7–12. European Language Resources Association (ELRA), Paris, France, May 2018

    Google Scholar 

  25. Pennell, D.L., Liu, Y.: A character-level machine translation approach for normalization of SMS abbreviations. In: IJCNLP, pp. 974–982 (2011)

    Google Scholar 

  26. Pennell, D.L., Liu, Y.: Normalization of informal text. Comput. Speech Lang. 28(1), 256–277 (2014)

    Article  Google Scholar 

  27. Qazi, A., Syed, K., Raj, R., Cambria, E., Tahir, M., Alghazzawi, D.: A concept-level approach to the analysis of online review helpfulness. Comput. Hum. Behav. 58, 75–81 (2016)

    Article  Google Scholar 

  28. Qian, T., Hollingshead, K., Yoon, S.Y., Kim, K.Y., Sproat, R.: A python toolkit for universal transliteration. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC 2010), pp. 2897–2901 (2010)

    Google Scholar 

  29. Rajagopal, D., Cambria, E., Olsher, D., Kwok, K.: A graph-based approach to commonsense concept extraction and semantic similarity detection. In: WWW, pp. 565–570 (2013)

    Google Scholar 

  30. Ramos, J., et al.: Using TF-IDF to determine word relevance in document queries. Proceedings of the first instructional conference on machine learning. 242, 133–142 (2003)

    Google Scholar 

  31. Rao, K., Peng, F., Sak, H., Beaufays, F.: Grapheme-to-phoneme conversion using long short-term memory recurrent neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4225–4229. IEEE (2015)

    Google Scholar 

  32. Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48. Association for Computational Linguistics (2005)

    Google Scholar 

  33. Rosa, K.D., Ellen, J.: Text classification methodologies applied to micro-text in military chat. In: Proceedings of the Eight International Conference on Machine Learning and Applications, Miami, pp. 710–714 (2009)

    Google Scholar 

  34. Satapathy, R., Guerreiro, C., Chaturvedi, I., Cambria, E.: Phonetic-based microtext normalization for twitter sentiment analysis. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 407–413. IEEE (2017)

    Google Scholar 

  35. Sproat, R., Black, A.W., Chen, S., Kumar, S., Ostendorf, M., Richards, C.: Normalization of non-standard words. Comput. Speech Lang. 15(3), 287–333 (2001)

    Article  Google Scholar 

  36. Toutanova, K., Moore, R.C.: Pronunciation modeling for improved spelling correction. In: ACL, pp. 144–151 (2002)

    Google Scholar 

  37. Vilares, D., Peng, H., Satapathy, R., Cambria, E.: Babelsenticnet: a commonsense reasoning framework for multilingual sentiment analysis. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1292–1298. IEEE (2018)

    Google Scholar 

  38. Wang, P., Ng, H.T.: A beam-search decoder for normalization of social media text with application to machine translation. In: HLT-NAACL, pp. 471–481 (2013)

    Google Scholar 

  39. Warschauer, M.: The internet and linguistic pluralism. Silicon literacies: Communication, innovation and education in the electronic age, pp. 62–74 (2002)

    Google Scholar 

  40. Xue, Z., Yin, D., Davison, B.D.: Normalizing Microtext. Analyzing Microtext, pp. 74–79 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Ranjan Satapathy & Erik Cambria

  2. Vellore Institute of Technology, Vellore, India

    Aalind Singh

Authors
  1. Ranjan Satapathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aalind Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Erik Cambria
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik Cambria .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Andrea Tagarelli

  2. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Hanghang Tong

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Satapathy, R., Singh, A., Cambria, E. (2019). PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34980-6_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34979-0

  • Online ISBN: 978-3-030-34980-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation