A Survey of Semantic Analysis Approaches

  • Said A. SalloumEmail author
  • Rehan Khan
  • Khaled Shaalan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)


Semantics is a branch of linguistics, which aims to investigate the meaning of language. Semantics deals with the meaning of sentences and words as fundamentals in the world. Semantic analysis within the framework of natural language processing evaluates and represents human language and analyzes texts written in the English language and other natural languages with the interpretation similar to those of human beings. This study aimed to critically review semantic analysis and revealed that explicit semantic analysis, latent semantic analysis, and sentiment analysis contribute to the leaning of natural languages and texts, enable computers to process natural languages, and reveal opinion attitudes in texts. The future prospect is in the domain of sentiment lexes. The overall results of the study were that semantics is paramount in processing natural languages and aid in machine learning. This study has covered various aspects including the Natural Language Processing (NLP), Latent Semantic Analysis (LSA), Explicit Semantic Analysis (ESA), and Sentiment Analysis (SA) in different sections of this study. However, LSA has been covered in detail with specific inputs from various sources. This study also highlights the future prospects of semantic analysis domain and finally the study is concluded with the result section where areas of improvement are highlighted and the recommendations are made for the future research. This study also highlights the weakness and the limitations of the study in the discussion (Sect. 4) and results (Sect. 5).


Natural language processing Sentiment analysis Explicit semantic analysis Latent semantic analysis Wikipedia linked based measures 


  1. 1.
    Alshurideh, M.T., Salloum, S.A., Al Kurdi, B., Monem, A.A., Shaalan, K.: Understanding the quality determinants that influence the intention to use the mobile learning platforms: a practical study. Int. J. Interact. Mob. Technol. 13(11), 157–183 (2019)CrossRefGoogle Scholar
  2. 2.
    Salloum, S.A.S., Shaalan, K.: Investigating students’ acceptance of e-learning system in higher educational environments in the UAE: applying the extended technology acceptance model (TAM). The British University in Dubai (2018)Google Scholar
  3. 3.
    Salloum, S.A., Al-Emran, M., Shaalan, K., Tarhini, A.: Factors affecting the E-learning acceptance: a case study from UAE. Educ. Inf. Technol. 24, 1–22 (2018)Google Scholar
  4. 4.
    Salloum, S.A., Alhamad, A.Q.M., Al-Emran, M., Monem, A.A., Shaalan, K.: Exploring students’ acceptance of E-learning through the development of a comprehensive technology acceptance model. IEEE Access 7, 128445–128462 (2019)CrossRefGoogle Scholar
  5. 5.
    Mhamdi, C., Al-Emran, M., Salloum, S.A.: Text mining and analytics: a case study from news channels posts on Facebook, vol. 740 (2018)Google Scholar
  6. 6.
    Salloum, S.A., AlHamad, A.Q., Al-Emran, M., Shaalan, K.: A survey of Arabic text mining, vol. 740 (2018)Google Scholar
  7. 7.
    Salloum, S.A., Al-Emran, M., Abdallah, S., Shaalan, K.: Analyzing the Arab Gulf newspapers using text mining techniques. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 396–405 (2017)Google Scholar
  8. 8.
    Salloum, S.A., Al-Emran, M., Shaalan, K.: Mining social media text: extracting knowledge from Facebook. Int. J. Comput. Digit. Syst. 6(2), 73–81 (2017)CrossRefGoogle Scholar
  9. 9.
    Salloum, S.A., Al-Emran, M., Monem, A.A., Shaalan, K.: Using text mining techniques for extracting information from research articles. In: Studies in Computational Intelligence, vol. 740. Springer (2018)Google Scholar
  10. 10.
    Salloum, S.A., Mhamdi, C., Al-Emran, M., Shaalan, K.: Analysis and classification of Arabic newspapers’ Facebook pages using text mining techniques. Int. J. Inf. Technol. Lang. Stud. 1(2), 8–17 (2017)Google Scholar
  11. 11.
    Salloum, S.A., Al-Emran, M., Shaalan, K.: Mining text in news channels: a case study from Facebook. Int. J. Inf. Technol. Lang. Stud. 1(1), 1–9 (2017)Google Scholar
  12. 12.
    Salloum, S.A., Al-Emran, M., Monem, A.A., Shaalan, K.: A survey of text mining in social media: Facebook and Twitter perspectives. Adv. Sci. Technol. Eng. Syst. J 2(1), 127–133 (2017)CrossRefGoogle Scholar
  13. 13.
    Chien, J.-T., Wu, M.-S.: Adaptive Bayesian latent semantic analysis. IEEE Trans. Audio. Speech. Lang. Processing 16(1), 198–207 (2007)CrossRefGoogle Scholar
  14. 14.
    Chang, C.-H., Kayed, M., Girgis, M.R., Shaalan, K.F.: A survey of web information extraction systems. IEEE Trans. Knowl. Data Eng. 18(10), 1411–1428 (2006)CrossRefGoogle Scholar
  15. 15.
    Al-Emran, M., Mezhuyev, V., Kamaludin, A., Shaalan, K.: The impact of knowledge management processes on information systems: a systematic review. Int. J. Inf. Manage. 43(July), 173–187 (2018)CrossRefGoogle Scholar
  16. 16.
    Evangelopoulos, N., Zhang, X., Prybutok, V.R.: Latent semantic analysis: five methodological recommendations. Eur. J. Inf. Syst. 21(1), 70–86 (2012)CrossRefGoogle Scholar
  17. 17.
    Kou, G., Peng, Y.: An application of latent semantic analysis for text categorization. Int. J. Comput. Commun. Control 10(3), 357–369 (2015)CrossRefGoogle Scholar
  18. 18.
    Alhashmi, S.F.S., Salloum, S. A., Abdallah, S.: Critical success factors for implementing artificial intelligence (AI) projects in dubai government United Arab Emirates (UAE) health sector: applying the extended technology acceptance model (TAM). In: International Conference on Advanced Intelligent Systems and Informatics, pp. 393–405(2019)Google Scholar
  19. 19.
    Rajani, S., Hanumanthappa, M.: Techniques of semantic analysis for natural language processing – a detailed survey. Int. J. Adv. Res. Comput. Commun. Eng. 5(2) (2016)Google Scholar
  20. 20.
    Farghaly, A., Shaalan, K.: Arabic natural language processing: challenges and solutions. ACM Trans. Asian Lang. Inf. Process. 8(4), 14 (2009)CrossRefGoogle Scholar
  21. 21.
    Liu, X., He, P., Chen, W., Gao, J.: Multi-task deep neural networks for natural language understanding. arXiv Prepr. arXiv1901.11504 (2019)Google Scholar
  22. 22.
    Cambria, E., White, B.: Jumping NLP curves: a review of natural language processing research. IEEE Comput. Intell. Mag. 9(2), 48–57 (2014)CrossRefGoogle Scholar
  23. 23.
    Mahesh, K., Nirenburg, S.: Semantic classification for practical natural language processing. In: Proceedings of Sixth ASIS SIG/CR Classification Research Workshop: An Interdisciplinary Meeting, pp. 116–139 (1995)Google Scholar
  24. 24.
    Chowdhury, G.G.: Natural language processing. Annu. Rev. Inf. Sci. Technol. 37(1), 51–89 (2003)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Calcagno, M.V., Barklund, P.J., Zhao, L., Azzam, S., Knoll, S.S., Chang, S.: Semantic analysis system for interpreting linguistic structures output by a natural language linguistic analysis system. Google Patents, 13 February 2007Google Scholar
  26. 26.
    Katayama, Y., Nakanishi, K., Yoshiura, H., Hirasawa, K.: System for processing natural language including identifying grammatical rule and semantic concept of an undefined word. Google Patents, 28 April 1992Google Scholar
  27. 27.
    Khan, A., Baharudin, B., Lee, L.H., Khan, K.: A review of machine learning algorithms for text-documents classification. J. Adv. Inf. Technol. 1(1), 4–20 (2010)Google Scholar
  28. 28.
    Hutchison, P.D., Daigle, R.J., George, B.: Application of latent semantic analysis in AIS academic research. Int. J. Account. Inf. Syst. 31, 83–96 (2018)CrossRefGoogle Scholar
  29. 29.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1–2), 177–196 (2001)CrossRefGoogle Scholar
  30. 30.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: IJcAI, vol. 7, pp. 1606–1611 (2007)Google Scholar
  31. 31.
    Witten, I.H., Milne, D.N.: An effective, low-cost measure of semantic relatedness obtained from Wikipedia links (2008)Google Scholar
  32. 32.
    Akerkar, R.: Natural language processing. In: Artificial Intelligence for Business, pp. 53–62. Springer (2019)Google Scholar
  33. 33.
    Mäntylä, M.V., Graziotin, D., Kuutila, M.: The evolution of sentiment analysis—a review of research topics, venues, and top cited papers. Comput. Sci. Rev. 27, 16–32 (2018)CrossRefGoogle Scholar
  34. 34.
    Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the 2nd International Conference on Knowledge Capture, pp. 70–77 (2003)Google Scholar
  35. 35.
    Abraham, A., Hassanien, A.-E., Snášel, V.: Computational Social Network Analysis: Trends. Tools and Research Advances. Springer, Heidelberg (2009)Google Scholar
  36. 36.
    Ghali, N., Panda, M., Hassanien, A.E., Abraham, A., Snasel, V.: Social networks analysis: tools, measures and visualization. In: Computational Social Networks, pp. 3–23. Springer (2012)Google Scholar
  37. 37.
    Salloum, S.A., Mhamdi, C., Al Kurdi, B., Shaalan, K.: Factors affecting the adoption and meaningful use of social media: a structural equation modeling approach. Int. J. Inf. Technol. Lang. Stud. 2(3), 96–109 (2018)Google Scholar
  38. 38.
    Al-Maroof, R.S., Salloum, S.A., AlHamadand, A.Q.M., Shaalan, K.: A unified model for the use and acceptance of stickers in social media messaging. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 370–381 (2019)Google Scholar
  39. 39.
    Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of Twitter data. In: Proceedings of the Workshop on Language in Social Media (LSM 2011), pp. 30–38 (2011)Google Scholar
  40. 40.
    Baets, W., Oldenboom, E., Hosken, C.: The potential of semantic analysis for business (education). Available SSRN 3364133 (2019)Google Scholar
  41. 41.
    Salloum, S.A., Al-Emran, M., Shaalan, K.: A survey of lexical functional grammar in the Arabic context. Int. J. Com. Net. Tech. 4(3), 141–147 (2016)Google Scholar
  42. 42.
    Godbole, N., Srinivasaiah, M., Skiena, S.: Large-scale Sentiment analysis for news and blogs. Icwsm 7(21), 219–222 (2007)Google Scholar
  43. 43.
    Delmonte, R. Pallotta, V.: Opinion mining and sentiment analysis need text understanding. In: Advances in Distributed Agent-Based Retrieval Tools, pp. 81–95. Springer (2011)Google Scholar
  44. 44.
    Al-Batah, M.S., Mrayyen, S., Alzaqebah, M.: Investigation of naive bayes combined with multilayer perceptron for arabic sentiment analysis and opinion mining. JCS 14(8), 1104–1114 (2018)Google Scholar
  45. 45.
    Chopra, F.K., Bhatia, R.: A critical review of sentiment analysis. Int. J. Comput. Appl. 149(10), 37–40 (2016)Google Scholar
  46. 46.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol. 10, no. 2010, pp. 1320–1326 (2010)Google Scholar
  47. 47.
    Rahate, R.S., Emmanuel, M.: Feature selection for sentiment analysis by using svm. Int. J. Comput. Appl. 84(5), 24–32 (2013)Google Scholar
  48. 48.
    Hofmann, T.: Probabilistic latent semantic analysis. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 289–296 (1999)Google Scholar
  49. 49.
    Medagoda, N., Shanmuganathan, S., Whalley, J.: A comparative analysis of opinion mining and sentiment classification in non-English languages. In: 2013 International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 144–148 (2013)Google Scholar
  50. 50.
    Mukherjee, S., Bhattacharyya, P.: Sentiment analysis in Twitter with lightweight discourse analysis. In: Proceedings of COLING 2012, pp. 1847–1864 (2012)Google Scholar
  51. 51.
    Helbig, H., Hartrumpf, S.: Word class functions for syntactic-semantic analysis. In: Proceedings of the 2nd International Conference on Recent Advances in Natural Language Processing (RANLP 1997), pp. 312–317 (1997)Google Scholar

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Authors and Affiliations

  1. 1.Faculty of Engineering and ITThe British University in DubaiDubaiUAE
  2. 2.Research Institute of Sciences and EngineeringUniversity of SharjahSharjahUAE

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