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Objectives: This study is aimed to develop a text preprocessing technique for mixed Bisaya and English short message service (SMS) messages. This technique is used to extract significant keywords for SMS message clustering procedure as the basis for SMS automated response on Higher Education Institution (HEI)’s enrollmentrelated inquiries. Methods/statistical analysis: In this study, a text clustering preprocessing technique is introduced and developed for mixed Bisaya and English SMS messages for Higher Education Institution (HEI) enrollment-related inquiries. The technique is a relatively new approach to extract significant keywords while addressing key challenges in morphological complexities on mixed Bisaya and English SMS messages. The method has seven (7) stages namely: tokenization, language tagging, stop-word removal, stemming, Soundex, final-tagging, and language translation. The term frequency co-occurrence clustering approach is applied to evaluate the precision and effectiveness of the text preprocessing technique. Findings: Test results revealed that the method produces a good preprocessing procedure with approximately 73%–83% accuracy rate on text processing and 87%–90% accuracy rate when text preprocessing is applied to clustering. Application/ improvements: The results of this study may assist academic institutions in maximizing the opportunity to effectively entertain more enrollment-related inquiries via SMS as an alternative communication medium to its target market. This also promotes technological advancement for the institution as it utilizes an ICTenhanced marketing approach through mobile technology.

indian journal of science and technology

A Text Clustering Preprocessing Technique for Mixed Bisaya and English Short Message Service (SMS) Messages for Higher Education Institutions (HEIs) Enrolment-Related Inquiries