Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
The advances of stemming algorithms in text analysis from 2013 to 2018
Dissertation (MCom)--University of Pretoria, 2019.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
University of Pretoria
2019
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613553843765248 |
|---|---|
| access_status_str | Open Access |
| author2 | Van Deventer, Jacobus Philippus (J.P.) |
| author_browse | Van Deventer, Jacobus Philippus (J.P.) |
| author_facet | Van Deventer, Jacobus Philippus (J.P.) |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MCom)--University of Pretoria, 2019. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/71712 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:58.997Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/71712 The advances of stemming algorithms in text analysis from 2013 to 2018 Van Deventer, Jacobus Philippus (J.P.) u13043707@tuks.co.za Kruger, Rendani Maarten Liu, Yi Yu UCTD Stemming Algorithms Text Mining Natural Language Processing (NLP) Text Analysis Information Retrieval Machine Learning Data Mining Engineering, built environment and information technology theses SDG-04 Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Dissertation (MCom)--University of Pretoria, 2019. Stemming is an activity within the pre-processing step of Text Analysis. It plays a role in the Text Analysis results. It drives Data Mining in fields such as Business Information Systems. Eight percent of existing organisational data that contributes Big Data is in an unstructured format. One of the focus areas within the concept of “Big Data” is the complexity of processing the data and being able to represent the results in such a way that they are easily understood. This challenge has been taken up by researchers over time. To determine the advances in Stemming Algorithm research, a systematic review was performed on articles on Stemming Algorithms published in journals from 2013 to 2018. Data was collected from accessible scholarly databases. The articles were then filtered by year and topic. The remaining articles were processed through a set of methodological quality criteria. The final articles were put through a bi-gram Text Analysis process to answer the research questions. The results concluded that the research focus for Stemming Algorithms has started to decrease as it reaches the plateau of productivity. The results show an evident drop in the collected articles from 58 in 2017 to 19 in 2018. Results show that information retrieval is still a common field of application for Stemming Algorithms. A major unexpected set of themes revolves around artificial intelligence, based on an increase in interest in this topic. Results show that a focus on Stemming Algorithms has shifted away from its development and moved towards its application. There is also a high interest in social media as an application of Stemming Algorithms. Future research suggestions include designing a Stemming Algorithm that would automatically and responsively adapt to the historical and morphological changes of language text. TM2019 es2026 Informatics MCom Unrestricted SDG-04: Quality education SDG-08: Decent work and economic growth SDG-09: Industry, innovation and infrastructure 2019-10-09T14:23:01Z 2019-10-09T14:23:01Z 19/09/03 2019 Dissertation Liu, YY 2019, The advances of stemming algorithms in text analysis from 2013 to 2018, MCom Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/71712> S2019 http://hdl.handle.net/2263/71712 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Stemming Algorithms Text Mining Natural Language Processing (NLP) Text Analysis Information Retrieval Machine Learning Data Mining Engineering, built environment and information technology theses SDG-04 Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 The advances of stemming algorithms in text analysis from 2013 to 2018 |
| title | The advances of stemming algorithms in text analysis from 2013 to 2018 |
| title_full | The advances of stemming algorithms in text analysis from 2013 to 2018 |
| title_fullStr | The advances of stemming algorithms in text analysis from 2013 to 2018 |
| title_full_unstemmed | The advances of stemming algorithms in text analysis from 2013 to 2018 |
| title_short | The advances of stemming algorithms in text analysis from 2013 to 2018 |
| title_sort | advances of stemming algorithms in text analysis from 2013 to 2018 |
| topic | UCTD Stemming Algorithms Text Mining Natural Language Processing (NLP) Text Analysis Information Retrieval Machine Learning Data Mining Engineering, built environment and information technology theses SDG-04 Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 |
| url | http://hdl.handle.net/2263/71712 |