Evaluation of Different Stemming Techniques on Arabic Customer Reviews
Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysi...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
middle technical university
2024-06-01
|
| Series: | Journal of Techniques |
| Subjects: | |
| Online Access: | https://journal.mtu.edu.iq/index.php/MTU/article/view/2313 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595168869482496 |
|---|---|
| author | Hawraa Fadhil Khelil Mohammed Fadhil Ibrahim Hafsa Ataallah Hussein Raed Kamil Naser |
| author_facet | Hawraa Fadhil Khelil Mohammed Fadhil Ibrahim Hafsa Ataallah Hussein Raed Kamil Naser |
| author_sort | Hawraa Fadhil Khelil |
| collection | DOAJ |
| description |
Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews in their dialectical style, which often diverts from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods can enhance classification performance despite the complexity of Arabic scripts and dialects. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results.
|
| format | Article |
| id | doaj-art-18a27a0d2b9b4bcc9a2946770470a7c3 |
| institution | Kabale University |
| issn | 1818-653X 2708-8383 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | middle technical university |
| record_format | Article |
| series | Journal of Techniques |
| spelling | doaj-art-18a27a0d2b9b4bcc9a2946770470a7c32025-01-19T10:58:54Zengmiddle technical universityJournal of Techniques1818-653X2708-83832024-06-016210.51173/jt.v6i2.2313Evaluation of Different Stemming Techniques on Arabic Customer ReviewsHawraa Fadhil Khelil0Mohammed Fadhil Ibrahim1Hafsa Ataallah Hussein2Raed Kamil Naser3Technical College of Management - Baghdad, Middle Technical University, Baghdad, Iraq.School of Computer Science, Universiti Sains Malaysia (USM), Penang, MalaysiaTechnical College of Management - Baghdad, Middle Technical University, Baghdad, Iraq.Universiti Sains Malaysia (USM), Penang, Malaysia Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews in their dialectical style, which often diverts from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods can enhance classification performance despite the complexity of Arabic scripts and dialects. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results. https://journal.mtu.edu.iq/index.php/MTU/article/view/2313NLPKNNNBLRSnowball StemmerKhoja Stemmer |
| spellingShingle | Hawraa Fadhil Khelil Mohammed Fadhil Ibrahim Hafsa Ataallah Hussein Raed Kamil Naser Evaluation of Different Stemming Techniques on Arabic Customer Reviews Journal of Techniques NLP KNN NB LR Snowball Stemmer Khoja Stemmer |
| title | Evaluation of Different Stemming Techniques on Arabic Customer Reviews |
| title_full | Evaluation of Different Stemming Techniques on Arabic Customer Reviews |
| title_fullStr | Evaluation of Different Stemming Techniques on Arabic Customer Reviews |
| title_full_unstemmed | Evaluation of Different Stemming Techniques on Arabic Customer Reviews |
| title_short | Evaluation of Different Stemming Techniques on Arabic Customer Reviews |
| title_sort | evaluation of different stemming techniques on arabic customer reviews |
| topic | NLP KNN NB LR Snowball Stemmer Khoja Stemmer |
| url | https://journal.mtu.edu.iq/index.php/MTU/article/view/2313 |
| work_keys_str_mv | AT hawraafadhilkhelil evaluationofdifferentstemmingtechniquesonarabiccustomerreviews AT mohammedfadhilibrahim evaluationofdifferentstemmingtechniquesonarabiccustomerreviews AT hafsaataallahhussein evaluationofdifferentstemmingtechniquesonarabiccustomerreviews AT raedkamilnaser evaluationofdifferentstemmingtechniquesonarabiccustomerreviews |