Advertisement

An automatic non-English sentiment lexicon builder using unannotated corpus

  • Mohammed Kaity
  • Vimala BalakrishnanEmail author
Article
  • 19 Downloads

Abstract

Sentiment lexicons in the English language are widely accessible while in many other languages, these resources are extremely deficient. Current techniques and methods for sentiment analysis focus mainly on the English language, whereas other languages are neglected due to lack of resources. In order to overcome challenges faced in building non-English lexicons, we propose a language-independent method that automatically builds non-English sentiment lexicons based on currently available English lexicons with an unannotated corpus from the target language. The proposed method will automatically recognize and extract new polarity words from the unannotated corpus based on the initial seed lexicons that are developed by translating three reliable English lexicons. The experimental results from the test datasets confirmed that a developed non-English sentiment lexicon could significantly enhance the performance of non-English sentiment classifications, compared with other methods and lexicons. The developed lexicon in the Arabic language outperformed other commonly used methods for developing non-English lexicons, with an 0.74 F measure. The adopted approach in this study was proven to be language independent and can be implemented in other languages as well. This paper also contributes to understanding the approaches to developing sentiment resources.

Keywords

Sentiment analysis Natural language processing Text analysis Sentiment lexicon Building resources 

References

  1. 1.
    Vilares D, Alonso MA, Gómez-Rodríguez C (2017) Supervised sentiment analysis in multilingual environments. Inf Process Manag 53(3):595–607CrossRefGoogle Scholar
  2. 2.
    Williams ML, Burnap P (2015) Cyberhate on social media in the aftermath of Woolwich: a case study in computational criminology and big data. Br J Criminol 56(2):211–238CrossRefGoogle Scholar
  3. 3.
    Bravo-Marquez F, Frank E, Pfahringer B (2016) Building a Twitter opinion lexicon from automatically-annotated tweets. Knowl Based Syst 108:65–78CrossRefGoogle Scholar
  4. 4.
    Wu FZ, Huang YF, Song YQ, Liu SX (2016) Towards building a high-quality microblog-specific Chinese sentiment lexicon. Decis Support Syst 87:39–49CrossRefGoogle Scholar
  5. 5.
    Kiritchenko S, Zhu X, Mohammad SM (2014) Sentiment analysis of short informal texts. J Artif Intell Res 50:723–762CrossRefGoogle Scholar
  6. 6.
    Deng S, Sinha AP, Zhao H (2017) Adapting sentiment lexicons to domain-specific social media texts. Decis Support Syst 94:65–76CrossRefGoogle Scholar
  7. 7.
    Bermingham A, Smeaton AF (2010) Classifying sentiment in microblogs: is brevity an advantage? In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, ACMGoogle Scholar
  8. 8.
    Chaovalit P, Zhou L (2005) Movie review mining: a comparison between supervised and unsupervised classification approaches. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences, 2005. HICSS’05, IEEEGoogle Scholar
  9. 9.
    Kouloumpis E, Wilson T, Moore JD (2011) Twitter sentiment analysis: the good the bad and the omg! ICWSM 11(538–541):164Google Scholar
  10. 10.
    Wu S-J, Chiang R-D, Ji Z-H (2017) Development of a Chinese opinion-mining system for application to Internet online forums. J Supercomput 73(7):2987–3001CrossRefGoogle Scholar
  11. 11.
    Lo SL, Cambria E, Chiong R, Cornforth D (2017) Multilingual sentiment analysis: from formal to informal and scarce resource languages. Artif Intell Rev 48(4):499–527CrossRefGoogle Scholar
  12. 12.
    Perez-Rosas V, Banea C, Mihalcea R (2012) Learning sentiment lexicons in Spanish. In: Lrec 2012: Eighth International Conference on Language Resources and Evaluation, 2012, pp 3077–3081Google Scholar
  13. 13.
    Steinberger J, Ebrahim M, Ehrmann M, Hurriyetoglu A, Kabadjov M, Lenkova P, Steinberger R, Tanev H, Vázquez S, Zavarella V (2012) Creating sentiment dictionaries via triangulation. Decis Support Syst 53(4):689–694CrossRefGoogle Scholar
  14. 14.
    Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167CrossRefGoogle Scholar
  15. 15.
    Lo SL, Cambria E, Chiong R, Cornforth D (2016) A multilingual semi-supervised approach in deriving Singlish sentic patterns for polarity detection. Knowl Based Syst 105:236–247CrossRefGoogle Scholar
  16. 16.
    Sun S, Luo C, Chen J (2017) A review of natural language processing techniques for opinion mining systems. Inf Fusion 36:10–25CrossRefGoogle Scholar
  17. 17.
    Dashtipour K, Poria S, Hussain A, Cambria E, Hawalah AY, Gelbukh A, Zhou Q (2016) Multilingual sentiment analysis: state of the art and independent comparison of techniques. Cogn Comput 8(4):757–771CrossRefGoogle Scholar
  18. 18.
    Abdaoui A, Azé J, Bringay S, Poncelet P (2017) Feel: a french expanded emotion lexicon. Lang Resour Eval 51(3):833–855CrossRefGoogle Scholar
  19. 19.
    Scharl A, Sabou M, Gindl S, Rafelsberger W, Weichselbraun A (2012) Leveraging the wisdom of the crowds for the acquisition of multilingual language resourcesGoogle Scholar
  20. 20.
    Hassan A, Abu-Jbara A, Jha R, Radev D (2011) Identifying the semantic orientation of foreign words. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, vol 2. Association for Computational LinguisticsGoogle Scholar
  21. 21.
    Nusko B, Tahmasebi N, Mogren O (2016) Building a sentiment lexicon for swedish. In: Digital Humanities 2016. From Digitization to Knowledge 2016: Resources and Methods for Semantic Processing of Digital Works/Texts, Proceedings of the Workshop, 11 July 2016, Krakow, Poland. Linköping University Electronic PressGoogle Scholar
  22. 22.
    Kumar P, Jaiswal UC (2016) A comparative study on sentiment analysis and opinion mining. Int J Eng Technol 8(2):938–943Google Scholar
  23. 23.
    Pozzi FA, Fersini E, Messina E, Liu B (2017) Chapter 1: challenges of sentiment analysis in social networks: an overview. sentiment analysis in social networks. Morgan Kaufmann, Boston, pp 1–11Google Scholar
  24. 24.
    Zhang HL, Gan WY, Jiang B (2014) IEEE, machine learning and lexicon based methods for sentiment classification: a survey. In: 2014 11th Web Information System and Application Conference (WISA), 2014, pp 262–265Google Scholar
  25. 25.
    Denecke K (2008) Using sentiwordnet for multilingual sentiment analysis. In: IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008, IEEEGoogle Scholar
  26. 26.
    Yao J, Wu G, Liu J, Zheng Y (2006) Using bilingual lexicon to judge sentiment orientation of Chinese words. In: The Sixth IEEE International Conference on Computer and Information Technology, 2006. CIT’06, IEEEGoogle Scholar
  27. 27.
    Mihalcea R, Banea C, Wiebe JM (2007) Learning multilingual subjective language via cross-lingual projectionsGoogle Scholar
  28. 28.
    Mohammad SM, Turney PD (2013) Crowdsourcing a word–emotion association lexicon. Comput Intell 29(3):436–465MathSciNetCrossRefGoogle Scholar
  29. 29.
    Nielsen FA (2011) A new ANEW: evaluation of a word list for sentiment analysis in microblogs. In: 1st Workshop on Making Sense of Microposts 2011: Big Things Come in Small Packages, #MSM 2011—Co-located with the 8th Extended Semantic Web Conference, ESWC 2011. Heraklion, CreteGoogle Scholar
  30. 30.
    Hammer H, Bai A, Yazidi A, Engelstad P (2014) Building sentiment lexicons applying graph theory on information from three norwegian thesauruses. In: Norsk Informatikkonferanse (NIK)Google Scholar
  31. 31.
    Basile V, Nissim M (2013) Sentiment analysis on Italian tweets. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisGoogle Scholar
  32. 32.
    Wilson T, Hoffmann P, Somasundaran S, Kessler J, Wiebe J, Choi Y, Cardie C, Riloff E, Patwardhan S (2005) OpinionFinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP on Interactive Demonstrations. Association for Computational LinguisticsGoogle Scholar
  33. 33.
    Remus R, Quasthoff U, Heyer G (2010) SentiWS: a publicly available German-language resource for sentiment analysis. In: LRECGoogle Scholar
  34. 34.
    Jha V, Savitha R, Hebbar SS, Shenoy PD, Venugopal K (2015) Hmdsad: Hindi multi-domain sentiment aware dictionary. In: International Conference on Computing and Network Communications (CoCoNet), 2015, IEEEGoogle Scholar
  35. 35.
    Al-Twairesh N, Al-Khalifa H, Al-Salman A (2016) AraSenTi: large-scale twitter-specific Arabic sentiment lexicons. In: Association for Computational Linguistics, 2016, pp 697–705Google Scholar
  36. 36.
    Elhawary M, Elfeky M (2010) Mining Arabic business reviews. In: IEEE International Conference on Data Mining Workshops (ICDMW), 2010, IEEEGoogle Scholar
  37. 37.
    Haniewicz K, Kaczmarek M, Adamczyk M, Rutkowski W (2014) Polarity lexicon for the polish language: design and extension with random walk algorithm. In: Swiatek J et al. (eds) International Conference on Systems Science, ICSS 2013, 2014. Springer, pp 173–182Google Scholar
  38. 38.
    Feng S, Song KS, Wang DL, Yu G (2015) A word-emoticon mutual reinforcement ranking model for building sentiment lexicon from massive collection of microblogs. World Wide Web-Internet Web Inf Syst 18(4):949–967CrossRefGoogle Scholar
  39. 39.
    Hong Y, Kwak H, Baek Y, Moon S (2013) Tower of babel: a crowdsourcing game building sentiment lexicons for resource-scarce languages. In: 22nd International Conference on World Wide Web, WWW 2013, Rio de JaneiroGoogle Scholar
  40. 40.
    Abdul-Mageed M, Diab M, Kübler S (2014) SAMAR: subjectivity and sentiment analysis for Arabic social media. Comput Speech Lang 28(1):20–37CrossRefGoogle Scholar
  41. 41.
    Lafourcade M, Joubert A, Le Brun N (2015) Collecting and evaluating lexical polarity with a game with a purpose. In: RANLPGoogle Scholar
  42. 42.
    Mohammad SM, Salameh M, Kiritchenko S (2016) How translation alters sentiment. J Artif Intell Res 55:95–130MathSciNetCrossRefGoogle Scholar
  43. 43.
    Shboul BA, Al-Ayyoub M, Jararweh Y (2015) Multi-way sentiment classification of Arabic reviews. In: 2015 6th International Conference on Information and Communication Systems (ICICS)Google Scholar
  44. 44.
    Abdullah M, Hadzikadic M (2017) Sentiment analysis on Arabic Tweets: challenges to dissecting the language. In: International Conference on Social Computing and Social Media, 2017. SpringerGoogle Scholar
  45. 45.
    Najar D, Mesfar S (2017) Opinion mining and sentiment analysis for Arabic on-line texts: application on the political domain. Int J Speech Technol 20:575–585CrossRefGoogle Scholar
  46. 46.
    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, ACMGoogle Scholar
  47. 47.
    Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, 2005. Association for Computational LinguisticsGoogle Scholar
  48. 48.
    Al-Moslmi T, Albared M, Al-Shabi A, Omar N, Abdullah S (2018) Arabic senti-lexicon: constructing publicly available language resources for Arabic sentiment analysis. J Inf Sci 44(3):345–362CrossRefGoogle Scholar
  49. 49.
    El-Halees A (2011) Arabic opinion mining using combined classification approach. In: The International Arab Conference on Information Technology, pp 10–13Google Scholar
  50. 50.
    Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol 61(12):2544–2558CrossRefGoogle Scholar
  51. 51.
    Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LRECGoogle Scholar
  52. 52.
    Black W, Elkateb S, Rodriguez H, Alkhalifa M, Vossen P, Pease A, Fellbaum C (2006) Introducing the Arabic wordnet project. In: Proceedings of the Third International WordNet ConferenceGoogle Scholar
  53. 53.
    Stone PJ, Dunphy DC, Smith MS (1966) The general inquirer: a computer approach to content analysis. MIT Press, OxfordGoogle Scholar
  54. 54.
    Mahyoub FHH, Siddiqui MA, Dahab MY (2014) Building an Arabic sentiment lexicon using semi-supervised learning. J King Saud Univ Comput Inf Sci 26(4):417–424Google Scholar
  55. 55.
    Badaro G, Baly R, Hajj H, Habash N, El-Hajj W (2014) A large scale Arabic sentiment lexicon for Arabic opinion mining. ANLP 2014:165Google Scholar
  56. 56.
    Maamouri M, Graff D, Bouziri B, Krouna S, Bies A, Kulick S (2010) Standard Arabic morphological analyzer (SAMA) version 3.1. Linguistic Data Consortium, Catalog No.: LDC2010L01Google Scholar
  57. 57.
    Esuli A, Sebastiani F (2007) SentiWordNet: a high-coverage lexical resource for opinion mining. Evaluation 17:1–26Google Scholar
  58. 58.
    Abdul-Mageed M, Diab MT (2014) SANA: a large scale multi-genre, multi-dialect lexicon for Arabic subjectivity and sentiment analysis. In: LREC, 2014Google Scholar
  59. 59.
    Abdul-Mageed M, MT Diab (2011) Subjectivity and sentiment annotation of modern standard arabic newswire. In: Proceedings of the 5th Linguistic Annotation Workshop, 2011. Association for Computational LinguisticsGoogle Scholar
  60. 60.
    Eskander R, Rambow O (2015) SLSA: a sentiment lexicon for Standard Arabic. In: Conference on Empirical Methods in Natural Language Processing, EMNLP 2015. Association for Computational Linguistics (ACL)Google Scholar
  61. 61.
    Buckwalter T (2002) Buckwalter Arabic morphological analyzer version 2.0. Linguistic Data Consortium, University of Pennsylvania, 2002. LDC Catalog No.: LDC2004L02. 2004, ISBN 1-58563-324-0Google Scholar
  62. 62.
    Al-Subaihin AA, Al-Khalifa HS, Al-Salman AS (2011) A proposed sentiment analysis tool for modern arabic using human-based computing. In: Proceedings of the 13th International Conference on Information Integration and Web-Based Applications and Services, 2011, ACMGoogle Scholar
  63. 63.
    Abdul-Mageed M (2019) Modeling Arabic subjectivity and sentiment in lexical space. Inf Process Manag 56(2):291–307CrossRefGoogle Scholar
  64. 64.
    Das SR, Chen MY (2007) Yahoo! for Amazon: sentiment extraction from small talk on the web. Manag Sci 53(9):1375–1388CrossRefGoogle Scholar
  65. 65.
    Velikovich L, Blair-Goldensohn S, Hannan K, McDonald R (2010) The viability of web-derived polarity lexicons. In: 2010 Human Language Technologies Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2010, Los Angeles, CAGoogle Scholar
  66. 66.
    Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307CrossRefGoogle Scholar
  67. 67.
    Davalos S, Merchant A, Rose GM, Lessley BJ, Teredesai AM (2015) ‘The good old days’: an examination of nostalgia in Facebook posts. Int J Hum Comput Stud 83:83–93CrossRefGoogle Scholar
  68. 68.
    Abdelali A, Darwish K, Durrani N, Mubarak H (2016) Farasa: a fast and furious segmenter for Arabic. In: HLT-NAACL Demos, 2016Google Scholar
  69. 69.
    Powers D (2007) Evaluation: from precision, recall and fmeasure to roc, informedness, markedness and correlation. J Mach Learn Technol 2:37–63Google Scholar
  70. 70.
    Giachanou A, Crestani F (2016) Like it or not: a survey of twitter sentiment analysis methods. ACM Comput Surv (CSUR) 49(2):28CrossRefGoogle Scholar
  71. 71.
    Mohammad SM, Turney PD (2013) Nrc emotion lexicon. 2013, NRC technical reportGoogle Scholar
  72. 72.
    Hussein DMEDM (2018) A survey on sentiment analysis challenges. J King Saud Univ Eng Sci 30(4):330–338Google Scholar
  73. 73.
    Saad MK (2010) The impact of text preprocessing and term weighting on arabic text classification. Comput Eng Islam Univ, GazaGoogle Scholar
  74. 74.
    Zerrouki T, Balla A (2017) Tashkeela: novel corpus of Arabic vocalized texts, data for auto-diacritization systems. Data Brief 11:147CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information System, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia

Personalised recommendations