Design of hybrid machine learning and TF-IDF models to discard irrelevant reviews on public transport stations
Users' opinions on social media about city landmarks are valuable tools for the responsible authorities. However, if the name of a city landmark is similar to a slightly related but different item, then users may be confused and inadvertently comment on the wrong item. Public transport stations...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-03-01
|
| Series: | Journal of Information and Telecommunication |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/24751839.2025.2472503 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849763371616829440 |
|---|---|
| author | Manuel Méndez Mercedes G. Merayo Manuel Núñez |
| author_facet | Manuel Méndez Mercedes G. Merayo Manuel Núñez |
| author_sort | Manuel Méndez |
| collection | DOAJ |
| description | Users' opinions on social media about city landmarks are valuable tools for the responsible authorities. However, if the name of a city landmark is similar to a slightly related but different item, then users may be confused and inadvertently comment on the wrong item. Public transport stations are a good example of this situation because the stations' names often refer to their location (e.g. a square, a neighbourhood, a hospital, etc.). In this paper, we use artificial intelligence models to develop a classification system that distinguishes reviews referring to the station itself from those that do not. To achieve this, we apply Natural Language Processing (NLP) techniques to numerically represent words and phrases, and artificial intelligence models to classify the text once it is numerically represented. Our experiments show that the combination of Term Frequency-Inverse Document Frequency (TF-IDF) and machine learning models, such as Support Vector Machine and Random Forest, yields the best results overall. To establish a precise setting for evaluating our system, we consider reviews on Google Maps about Madrid metro stations. However, our methodology should be easily extrapolated to other transport networks. |
| format | Article |
| id | doaj-art-c10cf4d6fa0f4ea6b6b637ee84756c0c |
| institution | DOAJ |
| issn | 2475-1839 2475-1847 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Information and Telecommunication |
| spelling | doaj-art-c10cf4d6fa0f4ea6b6b637ee84756c0c2025-08-20T03:05:26ZengTaylor & Francis GroupJournal of Information and Telecommunication2475-18392475-18472025-03-0112410.1080/24751839.2025.2472503Design of hybrid machine learning and TF-IDF models to discard irrelevant reviews on public transport stationsManuel Méndez0Mercedes G. Merayo1Manuel Núñez2Design and Testing of Reliable Systems Research Group, Complutense University of Madrid, Madrid, SpainDesign and Testing of Reliable Systems Research Group, Complutense University of Madrid, Madrid, SpainDesign and Testing of Reliable Systems Research Group, Complutense University of Madrid, Madrid, SpainUsers' opinions on social media about city landmarks are valuable tools for the responsible authorities. However, if the name of a city landmark is similar to a slightly related but different item, then users may be confused and inadvertently comment on the wrong item. Public transport stations are a good example of this situation because the stations' names often refer to their location (e.g. a square, a neighbourhood, a hospital, etc.). In this paper, we use artificial intelligence models to develop a classification system that distinguishes reviews referring to the station itself from those that do not. To achieve this, we apply Natural Language Processing (NLP) techniques to numerically represent words and phrases, and artificial intelligence models to classify the text once it is numerically represented. Our experiments show that the combination of Term Frequency-Inverse Document Frequency (TF-IDF) and machine learning models, such as Support Vector Machine and Random Forest, yields the best results overall. To establish a precise setting for evaluating our system, we consider reviews on Google Maps about Madrid metro stations. However, our methodology should be easily extrapolated to other transport networks.https://www.tandfonline.com/doi/10.1080/24751839.2025.2472503Machine learningdeep learningnatural language processingclassification of comments on social networks |
| spellingShingle | Manuel Méndez Mercedes G. Merayo Manuel Núñez Design of hybrid machine learning and TF-IDF models to discard irrelevant reviews on public transport stations Journal of Information and Telecommunication Machine learning deep learning natural language processing classification of comments on social networks |
| title | Design of hybrid machine learning and TF-IDF models to discard irrelevant reviews on public transport stations |
| title_full | Design of hybrid machine learning and TF-IDF models to discard irrelevant reviews on public transport stations |
| title_fullStr | Design of hybrid machine learning and TF-IDF models to discard irrelevant reviews on public transport stations |
| title_full_unstemmed | Design of hybrid machine learning and TF-IDF models to discard irrelevant reviews on public transport stations |
| title_short | Design of hybrid machine learning and TF-IDF models to discard irrelevant reviews on public transport stations |
| title_sort | design of hybrid machine learning and tf idf models to discard irrelevant reviews on public transport stations |
| topic | Machine learning deep learning natural language processing classification of comments on social networks |
| url | https://www.tandfonline.com/doi/10.1080/24751839.2025.2472503 |
| work_keys_str_mv | AT manuelmendez designofhybridmachinelearningandtfidfmodelstodiscardirrelevantreviewsonpublictransportstations AT mercedesgmerayo designofhybridmachinelearningandtfidfmodelstodiscardirrelevantreviewsonpublictransportstations AT manuelnunez designofhybridmachinelearningandtfidfmodelstodiscardirrelevantreviewsonpublictransportstations |