Abstract
In this paper, two models are proposed to automatically categorize the farmers’ complaints on various diseases that may affect crops. In the first model, a complaint which is expressed in Arabic free text is classified into its respective crop and then into a particular disease, but in the second model, the complaint is classified directly into diseases. A dataset of farmers’ complaints described in Arabic script consisting of complaints from five different crops raised by farmers of different parts of Egypt is created. Separate lexicons are created for crops and their diseases by considering all possible technical terms related to a crop and its diseases including their possible slang synonyms. Each preprocessed complaint is represented in the form of a binary vector using the vector space model (VSM) with the help of crop lexicon so that machine learning techniques can be applied. Experiments are conducted on the dataset by varying the percentage of training with multiple trials using SVM and KNN classifiers. It has been observed from the results that the proposed model is performing on par with the human expert and can be deployable for real-time operations.
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Park, K., et al.: Mining the minds of customers from online chat logs. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1879–1882 (2015)
El-Beltagy, S.R., Rafea, A., Mabrouk, S., Rafea, M.: Mining farmers problems in web based textual database application. In: 12th International Conference on Enterprise Information Systems, ICEIS (2010)
El-Beltagy, S.R., Rafea, A., Mabrouk, S., Rafea, M.: An approach for mining accumulated crop cultivation problems and their solutions. In: Proceedings of the International Computer Engineering Conference (ICENCO) (2010)
Zaghoul, F.A., Al-Dhaheri, S.: Arabic text classification based on features reduction using artificial neural networks. In: UKSim 15th International Conference on Computer Modelling and Simulation (UK-Sim). IEEE (2013)
Shoukry, A., Rafea, A.: A hybrid approach for sentiment classification of Egyptian dialect tweets. In: First International Conference on Arabic Computational Linguistics (ACLing), pp. 78–85 (2015)
Ahmed, N., Shehab, M., Al-Ayyoub, M., Hmeidi, I.: Scalable multi-label Arabic text classification. In: The International Conference on Information and Communication Systems (ICICS) (2015)
Khreisat, L.: Arabic text classification using N-gram frequency statistics: a comparative study. In: Proceedings of the DMIN 2006, Las Vegas, USA (2006)
Ismail, H., Bilal, H., Eyas, E.-Q.: Performance of KNN and SVM classifiers on full word Arabic articles. Adv. Eng. Inform. 22(1), 106–111 (2008)
Guru, D.S., Suhil, M.: A novel term\(\_\)class relevance measure for text categorization. Procedia Comput. Sci. 45, 13–22 (2015)
Al-Shalabiand, R., Obeidat, R.: Improving KNN Arabic text classification with N-grams based document indexing (2008)
Mesleh, A.M.: Chi square feature extraction based SVMs Arabic language text categorization system. J. Comput. Sci. 3(6), 430 (2007)
Guru, D.S., Ali, M., Suhil, M.: A novel term weighting scheme and an approach for classification of agricultural Arabic text complaints. In: 2nd IEEE International Workshop on Arabic and derived Script Analysis and Recognition (ASAR), pp. 24–28 (2018)
Guru, D.S., Ali, M., Suhil, M.: A novel feature selection technique for text classification. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds.) Emerging Technologies in Data Mining and Information Security, vol. 813, pp. 721–733. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1498-8_63
Guru, D.S., Ali, Mostafa, Suhil, Mahamad, Hazman, Maryam: A Study of Applying Different Term Weighting Schemes on Arabic Text Classification. In: Nagabhushan, P., Guru, D.S., Shekar, B.H., Kumar, Y.H.Sharath (eds.) Data Analytics and Learning. LNNS, vol. 43, pp. 293–305. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-2514-4_25
Hassan, T., Soliman, A., Ali, M.A.: Mining social networks’ Arabic slang comments. In: Proceedings of IADIS European Conference on Data Mining (ECDM 2013), pp. 22–24 (2013)
http://www.arc.sci.eg/NARIMS\(\_\)upload/NARIMSdocs/79523/ 2010.pdf
http://www.vercon.sci.eg/indexUI/uploaded/wheatinoldsoil/wheatinoldsoil.htm
Tutubalina, Elena: Target-Based Topic Model for Problem Phrase Extraction. In: Hanbury, Allan, Kazai, Gabriella, Rauber, Andreas, Fuhr, Norbert (eds.) ECIR 2015. LNCS, vol. 9022, pp. 271–277. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16354-3_29
Mansour, N., Haraty, R.A., Daher, W., Houri, M.: An auto-indexing method for Arabic text. J. Inf. Process. Manag. 44(4), 1538–1545 (2008)
Lee, A.J.T., Yang, F.-C., Chen, C.-H., Wang, C.-S., Sun, C.-Y.: Mining perceptual maps from consumer reviews. J. Decis. Support Syst. 82, 12–25 (2016)
Haddi, E., Liu, X., Shi, Y.: The role of text pre-processing in sentiment analysis. Procedia Comput. Sci. 17, 26–32 (2013). Information Technology and Quantitative Management (ITQM)
Khoja, S., Garside, R.: Stemming Arabic text. Lancaster University, Lancaster, UK, Computing Department (1999)
Elbeltagy, S.R., Reafea, A.: An accuracy-enhanced light stemmer for Arabic text. ACM Trans. Speech Lang. Process. (TSLP) 7(2), 1–22 (2011)
Dong, S., Wang, Z.: Evaluating service quality in insurance customer complaint handling throught text categorization. In: International Conference on Logistics, Informatics and Service Sciences (LISS), 27–29 July 2015, pp. 1–5 (2015)
Georgiou, T., Abbadi, A.E., Yan, X., George, J.: Mining complaints for traffic-jam estimation: a social sensor application. Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, 330–335 (2015)
Lee, C.-H., Wang, Y.-H., Trappey, A.J.C.: Ontology-based reasoning for the intelligent handling of customer complaints. J. Comput. Ind. Eng. 84(C), 144–155 (2015)
Zirtiloglu, H., Yolum, P.: Ranking semantic information for e-government: complaints management. In: Proceedings of the First International Workshop on Ontology-Supported Business Intelligence, OBI 2008. ACM (2008). Article no. 5
Al Zamil, M.G.H., Al-Radaideh, Q.: Automatic extraction of ontological relations from Arabic text. J. King Saud Univ. Comput. Inf. Sci. 28(1), 1–146 (2016)
Liang, Y.H.: Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Syst. Appl. 37, 7489–7496 (2010)
Lan, M., Tan, C.L., Su, J., Lu, Y.: Supervised and traditional term weighting methods for automatic text categorization. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 721–735 (2009)
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Ali, M., Guru, D.S., Suhil, M. (2019). Classifying Arabic Farmers’ Complaints Based on Crops and Diseases Using Machine Learning Approaches. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_38
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DOI: https://doi.org/10.1007/978-981-13-9187-3_38
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