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Sentiment Analysis Online Tools: An Evaluation Study

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International Conference on Information Systems and Intelligent Applications (ICISIA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 550))

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Abstract

A sentiment analysis tool interprets text chats and assesses each opinion’s style, purpose, and feeling. The tool can better understand the context of users’ discussions, allowing the client service team to classify client feedback accurately. This is especially valuable for companies that actively address clients’ inquiries and complaints on social media, live chat, and email. Despite its vitality for business, there is still a challenge to decide the sentiment behind the content, especially for the Arabic language. Although most are not available for public usage, many sentiment analysis models and tools are developed in the literature. However, there is a lack of research identifying these tools’ practicality for the Arabic language. This paper investigates two pure online Arabic sentiment analysis tools by employing a sizeable Arabic dataset in the experiments. Prediction quality measurements were utilized to assess these tools. The yielded results recommended Sentest SA as a promised tool for detecting sentiment analysis polarity for the preprocessed Arabic social network contents.

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References

  1. Aakanksha S, Sinha GR, Bhatia S (2021) New opportunities for sentiment analysis and information processing. IGI Global

    Google Scholar 

  2. Al-Kabi M, Alsmadi I, Khasawneh RT, Wahsheh H (2018) Evaluating social context in Arabic opinion mining. Int Arab J Inf Technol 15:974–982

    Google Scholar 

  3. Al-Kabi MN, Wahsheh HA, Alsmadi IM (2016) Polarity classification of Arabic sentiments. Int J Inf Technol Web Eng 11:32–49

    Article  Google Scholar 

  4. Radcliffe D, Abuhmaid H (2021) How the Middle East used social media in 2020. Available at SSRN 3826011

    Google Scholar 

  5. Mansoor M, Gurumurthy K, Prasad V, et al (2020) Global sentiment analysis of COVID-19 tweets over time. arXiv preprint arXiv:2010.14234

  6. Alamoodi A, Zaidan BB, Zaidan AA, et al (2021) Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: a systematic review. Expert Syst Appl 167:114155

    Google Scholar 

  7. Doaa Soliman (2021) Social media: a decade of leading change in the Arab world. https://p.dw.com/p/3ukhk. Accessed 1 Apr 2022

  8. Valle-Cruz D, Fernandez-Cortez V, López-Chau A, Sandoval-Almazán R (2022) Does twitter affect stock market decisions? Financial sentiment analysis during pandemics: a comparative study of the H1N1 and the covid-19 periods. Cogn Comput 14:372–387

    Article  Google Scholar 

  9. Lee SW, Jiang G, Kong HY, Liu C (2021) A difference of multimedia consumer’s rating and review through sentiment analysis. Multimedia Tools Appl 80:34625–34642

    Article  Google Scholar 

  10. Ligthart A, Catal C, Tekinerdogan B (2021) Systematic reviews in sentiment analysis: a tertiary study. Artif Intell Rev 54:4997–5053

    Article  Google Scholar 

  11. Dolianiti FS, Iakovakis D, Dias SB (2019) Sentiment analysis on educational datasets: a comparative evaluation of commercial tools. Educ J Univ Patras UNESCO Chair

    Google Scholar 

  12. Khasawneh RT, Wahsheh HA, Alsmadi IM, AI-Kabi MN (2015) Arabic sentiment polarity identification using a hybrid approach. In: 2015 6th International Conference on Information and Communication Systems (ICICS). IEEE, pp 148–153

    Google Scholar 

  13. Al-Kabi M, Al-Qudah NM, Alsmadi I, Dabour M, Wahsheh H (2013) Arabic/English sentiment analysis: an empirical study. In: The Fourth International Conference on Information and Communication Systems (ICICS 2013), pp 23–25

    Google Scholar 

  14. Al-Kabi MN, Gigieh AH, Alsmadi IM, Wahsheh HA, Haidar MM (2014) Opinion mining and analysis for Arabic language. Int J Adv Comput Sci Appl 5:181–195

    Google Scholar 

  15. Khasawneh RT, Wahsheh HA, Al-Kabi MN, Alsmadi IM (2013) Sentiment analysis of Arabic social media content: a comparative study. In: 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013). IEEE, pp 101–106

    Google Scholar 

  16. Al-Kabi M, Gigieh A, Alsmadi I, Wahsheh H, Haidar M (2013) An opinion analysis tool for colloquial and standard Arabic. In: The Fourth International Conference on Information and Communication Systems (ICICS 2013), pp 23–25

    Google Scholar 

  17. Al-Ayyoub M, Khamaiseh AA, Jararweh Y, Al-Kabi MN (2019) A comprehensive survey of Arabic sentiment analysis. Inf Process Manag 56:320–342

    Article  Google Scholar 

  18. Rabab’Ah, AM, Al-Ayyoub M, Jararweh Y, Al-Kabi MN (2016) Evaluating sentistrength for Arabic sentiment analysis. In: 2016 7th International Conference on Computer Science and Information Technology (CSIT). IEEE, pp 1–6

    Google Scholar 

  19. Khafajeh H (2020) Opinion mining: How efficient are Online classification tools? Int J Emerg Trends Eng Res 8:557–567

    Google Scholar 

  20. Ali Salhi (2022) Arabic tools. https://www.arabitools.com/sentest.html. Accessed 1 Apr 2022

  21. Farha IA, Magdy W (2019) Mazajak: an online Arabic sentiment analyser. In: Proceedings of the Fourth Arabic Natural Language Processing Workshop, pp 192–198

    Google Scholar 

  22. Al-Kabi M, Al-Ayyoub M, Alsmadi I, Wahsheh H (2016) A prototype for a standard Arabic sentiment analysis corpus. Int Arab J Inf Technol 13:163–170

    Google Scholar 

  23. Abdulla NA, Ahmed NA, Shehab MA, Al-Ayyoub M (2013) Arabic sentiment analysis: Lexicon-based and corpus-based. In: 2013 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT). IEEE, pp. 1–6

    Google Scholar 

  24. Najadat H, Alzubaidi MA, Qarqaz I (2021) Detecting Arabic spam reviews in social networks based on classification algorithms. Trans Asian Low-Res Lang Inf Process 21:1–13

    Google Scholar 

  25. Al-Kabi MN, Alsmadi IM, Wahsheh HA (2015) Evaluation of spam impact on Arabic websites popularity. J King Saud Univ Comput Inf Sci 27:222–229

    Google Scholar 

  26. Al-Kabi MN, Wahsheh HA, Alsmadi IM (2014) OLAWSDS: an online Arabic web spam detection system. Int J Adv Comput Sci Appl 5:105–110

    Google Scholar 

  27. Wahsheh HA, Al-Kabi MN, Alsmadi IM (2013) SPAR: a system to detect spam in Arabic opinions. In: 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). IEEE, pp 1–6

    Google Scholar 

  28. Alsmadi I, Al-Kabi MN, Wahsheh H, Bassam B (2013) Video spam and public opinion in current middle eastern conflicts. Int J Soc Netw Mining 1:318–333

    Article  Google Scholar 

  29. Saeed RM, Rady S, Gharib TF (2022) An ensemble approach for spam detection in Arabic opinion texts. J King Saud Univ Comput Inf Sci 34:1407–1416

    Google Scholar 

  30. Abu-Salih B, Qudah DA, Al-Hassan M et al (2022) An intelligent system for multi-topic social spam detection in microblogging. arXiv preprint arXiv:2201.05203

  31. Wahsheh HA, Al-Kabi MN, Alsmadi IM (2013) A link and content hybrid approach for Arabic web spam detection. Int J Intell Syst Appl 5(1):30–43

    Google Scholar 

  32. Sahoo SR, Gupta BB, Peraković D, Peñalvo FJG, Cvitić I (2022) Spammer detection approaches in online social network (OSNs): a survey. In: Sustainable Management of Manufacturing Systems in Industry 4.0. Springer, Cham, pp 159–180

    Google Scholar 

  33. Alsmadi M, Alsmadi I, Wahsheh HA (2022) URL links malicious classification towards autonomous threat detection systems. International Conference on Emerging Technologies and Intelligent Systems. Springer, pp 497–506

    Google Scholar 

  34. Al-Emran M, Zaza S, Shaalan K (2015) Parsing modern standard Arabic using Treebank resources. In: 2015 International Conference on Information and Communication Technology Research (ICTRC). IEEE, pp 80–83

    Google Scholar 

  35. Salloum SA, Al-Emran M, Shaalan K (2016) A survey of lexical functional grammar in the Arabic context. Int J Comput Netw Technol 4(3)

    Google Scholar 

  36. Salloum SA, Al-Emran M, Shaalan K (2017) Mining text in news channels: a case study from Facebook. Int J Inf Technol Lang Stud 1(1):1–9

    Google Scholar 

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Acknowledgements

The authors acknowledge King Faisal University for the financial support.

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Authors and Affiliations

  1. Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa, 31982, Saudi Arabia

    Heider A. M. Wahsheh & Abdulaziz Saad Albarrak

Authors
  1. Heider A. M. Wahsheh
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  2. Abdulaziz Saad Albarrak
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Corresponding author

Correspondence to Heider A. M. Wahsheh .

Editor information

Editors and Affiliations

  1. The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  2. Sunway University, Selangor, Malaysia

    Mohammed A. Al-Sharafi

  3. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

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Wahsheh, H.A.M., Albarrak, A.S. (2023). Sentiment Analysis Online Tools: An Evaluation Study. In: Al-Emran, M., Al-Sharafi, M.A., Shaalan, K. (eds) International Conference on Information Systems and Intelligent Applications. ICISIA 2022. Lecture Notes in Networks and Systems, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-031-16865-9_9

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