Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text

Wegderes Tariku; Million Meshesha; Ashebir Hunegnaw; Kedir Lemma

doi:doi:10.11648/j.ajesa.20220902.12

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Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text

Wegderes Tariku, Million Meshesha, Ashebir Hunegnaw, Kedir Lemma

Published in American Journal of Embedded Systems and Applications (Volume 9, Issue 2)

Received: 17 August 2022 Accepted: 7 September 2022 Published: 19 September 2022

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Abstract

Studying the specific subject of opinion mining has been a popular research area as a means of overcoming the challenge of user-generated content on the web, which can be challenging to manually collect, comprehend, summarize, and analyze for decision-making. Even though there are three various levels at which opinion mining can be done, the detail and complexity of feature level opinion mining outweighs its disadvantages. The goal of this research is to provide sentiment mining and aspect-based opinion summaries of service reviews in Afaan Oromo for Oromia Radio and Television Organization (ORTO). 400 reviews in all were gathered and used for news-related purposes from ORTO. The model has five elements, including document inspection, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summary, as well as a bar chart to show aspect-based sentiment summation. Five different processes make up the model: document review, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summarization. A bar chart is also utilized to visually depict aspect-based opinion polarity. For positive classes, 90% precision and 87% recall are accomplished, while for negative classes, 87% precision and 89.7% recall are attained. The main issue identified in this study is that users tend to express their opinions in a context-based or indirect manner. They could express their negative feelings with pleasant words or the opposite. Therefore, more research is required before the algorithm will take context-based or semantic opinion mining into account.

Published in	American Journal of Embedded Systems and Applications (Volume 9, Issue 2)
DOI	10.11648/j.ajesa.20220902.12
Page(s)	66-72
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Opinionated Afaan Oromo News Texts, Aspect Level Sentiment Mining, Sentiment Summarization, Lexical Database, Oromia Radio and Television Organization

References

[1]	B. Liu, "Sentiment Analysis and Opinion Mining," Morgan & Claypool Publishers, April 22, 2012.
[2]	Bo Pang and Lillian Lee, "Opinion mining and sentiment analysis," Foundation and Trends in Information Retrieval, vol. 2, pp. 1-135, 2008.
[3]	B. Liu, "Sentiment Analysis," 5th Text Analytics Summit, Boston, pp. 1-2, June 2009.
[4]	Lei Zhang and Bing Liu, "Aspect and Entity Extraction for Opinion Mining," Data mining and knowledge discovery for big data, pp. 1-40, 2014.
[5]	G. Vinodhini and R M. Chandrasekaran, "Sentiment analysis and Opinion Mining: A survey," International Journal of Advanced Resarch in Computer Science and Software Engineering, vol. 2, no. 6, pp. 1-11, June 2012.
[6]	L. Zhang, "Aspect and Entity Extraction from Opinion Documents," 2012.
[7]	E. Akba, "Aspect based opinion mining on turkish tweets," Bilkent university, 2012.
[8]	Alasmar, Ahmed M., "Feature Based Approach in Arabic Opinion Mining Using Ontology," 2016.
[9]	T. T. Hailu, "Opinion Mining from Amharic Blog," Ethiopia, 2013.
[10]	E. Alamerew, "Automatic Annotation of Opinionated Amharic Text For Opinion Mining," Ethiopia, 2016.
[11]	S. Gebremeskel, "Sentiment Mining Model for Opinionated Amharic Texts," Ethiopia, 2010.
[12]	Noah Smith and David Smit, 2005. [Online]. Available: http://www.cs.jhu.edu/~nasmith/erm/ (Accessed date 4/6/2017). [Accessed 4 February 2017].
[13]	Mcsherry, Frank and Marc Najork, "Computing information retrieval performance measures efficiently in the presence of tied scores," Advances in information retrieval, pp. 414-421, 2008.
[14]	A. Getachew, "Opinion Mining from Amharic Entertainment Texts," Ethiopia, October, 2014.
[15]	M. Tune, "Designing A Graph-Based Opinion Mining Model for Opinionated Text in English, Amharic and Afaan Oromo Language," Ethiopia, February 2015.
[16]	"Substitutions and sentence splitting," Pandorabots, 2017. [Online]. Available: http://docs.pandorabots.com/tutorials/substitutions-and-sentence-splitting/. [Accessed 5 4 2017].
[17]	D. Tesfaye, "Designing a Stemmer for Afan Oromo Text: A hybrid approach".

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APA Style

Wegderes Tariku, Million Meshesha, Ashebir Hunegnaw, Kedir Lemma. (2022). Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text. American Journal of Embedded Systems and Applications, 9(2), 66-72. https://doi.org/10.11648/j.ajesa.20220902.12

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ACS Style

Wegderes Tariku; Million Meshesha; Ashebir Hunegnaw; Kedir Lemma. Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text. Am. J. Embed. Syst. Appl. 2022, 9(2), 66-72. doi: 10.11648/j.ajesa.20220902.12

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AMA Style

Wegderes Tariku, Million Meshesha, Ashebir Hunegnaw, Kedir Lemma. Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text. Am J Embed Syst Appl. 2022;9(2):66-72. doi: 10.11648/j.ajesa.20220902.12

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@article{10.11648/j.ajesa.20220902.12,
  author = {Wegderes Tariku and Million Meshesha and Ashebir Hunegnaw and Kedir Lemma},
  title = {Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {9},
  number = {2},
  pages = {66-72},
  doi = {10.11648/j.ajesa.20220902.12},
  url = {https://doi.org/10.11648/j.ajesa.20220902.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20220902.12},
  abstract = {Studying the specific subject of opinion mining has been a popular research area as a means of overcoming the challenge of user-generated content on the web, which can be challenging to manually collect, comprehend, summarize, and analyze for decision-making. Even though there are three various levels at which opinion mining can be done, the detail and complexity of feature level opinion mining outweighs its disadvantages. The goal of this research is to provide sentiment mining and aspect-based opinion summaries of service reviews in Afaan Oromo for Oromia Radio and Television Organization (ORTO). 400 reviews in all were gathered and used for news-related purposes from ORTO. The model has five elements, including document inspection, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summary, as well as a bar chart to show aspect-based sentiment summation. Five different processes make up the model: document review, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summarization. A bar chart is also utilized to visually depict aspect-based opinion polarity. For positive classes, 90% precision and 87% recall are accomplished, while for negative classes, 87% precision and 89.7% recall are attained. The main issue identified in this study is that users tend to express their opinions in a context-based or indirect manner. They could express their negative feelings with pleasant words or the opposite. Therefore, more research is required before the algorithm will take context-based or semantic opinion mining into account.},
 year = {2022}
}

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TY  - JOUR
T1  - Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text
AU  - Wegderes Tariku
AU  - Million Meshesha
AU  - Ashebir Hunegnaw
AU  - Kedir Lemma
Y1  - 2022/09/19
PY  - 2022
N1  - https://doi.org/10.11648/j.ajesa.20220902.12
DO  - 10.11648/j.ajesa.20220902.12
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 66
EP  - 72
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20220902.12
AB  - Studying the specific subject of opinion mining has been a popular research area as a means of overcoming the challenge of user-generated content on the web, which can be challenging to manually collect, comprehend, summarize, and analyze for decision-making. Even though there are three various levels at which opinion mining can be done, the detail and complexity of feature level opinion mining outweighs its disadvantages. The goal of this research is to provide sentiment mining and aspect-based opinion summaries of service reviews in Afaan Oromo for Oromia Radio and Television Organization (ORTO). 400 reviews in all were gathered and used for news-related purposes from ORTO. The model has five elements, including document inspection, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summary, as well as a bar chart to show aspect-based sentiment summation. Five different processes make up the model: document review, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summarization. A bar chart is also utilized to visually depict aspect-based opinion polarity. For positive classes, 90% precision and 87% recall are accomplished, while for negative classes, 87% precision and 89.7% recall are attained. The main issue identified in this study is that users tend to express their opinions in a context-based or indirect manner. They could express their negative feelings with pleasant words or the opposite. Therefore, more research is required before the algorithm will take context-based or semantic opinion mining into account.
VL  - 9
IS  - 2
ER  -

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Author Information

Wegderes Tariku

Department of Information Systems, Mizan Tepi University, Tepi, Ethiopia
Million Meshesha

Department of Information Science Addis Ababa University, Addis Ababa, Ethiopia
Ashebir Hunegnaw

Department of Management Information Systems, Mettu University, Mettu, Ethiopia
Kedir Lemma

Deprtment of Information Technology Ambo University, Ambo, Ethiopia

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APA Style

Wegderes Tariku, Million Meshesha, Ashebir Hunegnaw, Kedir Lemma. (2022). Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text. American Journal of Embedded Systems and Applications, 9(2), 66-72. https://doi.org/10.11648/j.ajesa.20220902.12

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ACS Style

Wegderes Tariku; Million Meshesha; Ashebir Hunegnaw; Kedir Lemma. Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text. Am. J. Embed. Syst. Appl. 2022, 9(2), 66-72. doi: 10.11648/j.ajesa.20220902.12

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AMA Style

Wegderes Tariku, Million Meshesha, Ashebir Hunegnaw, Kedir Lemma. Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text. Am J Embed Syst Appl. 2022;9(2):66-72. doi: 10.11648/j.ajesa.20220902.12

Copy | Download

@article{10.11648/j.ajesa.20220902.12,
  author = {Wegderes Tariku and Million Meshesha and Ashebir Hunegnaw and Kedir Lemma},
  title = {Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {9},
  number = {2},
  pages = {66-72},
  doi = {10.11648/j.ajesa.20220902.12},
  url = {https://doi.org/10.11648/j.ajesa.20220902.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20220902.12},
  abstract = {Studying the specific subject of opinion mining has been a popular research area as a means of overcoming the challenge of user-generated content on the web, which can be challenging to manually collect, comprehend, summarize, and analyze for decision-making. Even though there are three various levels at which opinion mining can be done, the detail and complexity of feature level opinion mining outweighs its disadvantages. The goal of this research is to provide sentiment mining and aspect-based opinion summaries of service reviews in Afaan Oromo for Oromia Radio and Television Organization (ORTO). 400 reviews in all were gathered and used for news-related purposes from ORTO. The model has five elements, including document inspection, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summary, as well as a bar chart to show aspect-based sentiment summation. Five different processes make up the model: document review, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summarization. A bar chart is also utilized to visually depict aspect-based opinion polarity. For positive classes, 90% precision and 87% recall are accomplished, while for negative classes, 87% precision and 89.7% recall are attained. The main issue identified in this study is that users tend to express their opinions in a context-based or indirect manner. They could express their negative feelings with pleasant words or the opposite. Therefore, more research is required before the algorithm will take context-based or semantic opinion mining into account.},
 year = {2022}
}

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TY  - JOUR
T1  - Sentiment Mining and Aspect Based Summarization of Opinionated Afaan Oromoo News Text
AU  - Wegderes Tariku
AU  - Million Meshesha
AU  - Ashebir Hunegnaw
AU  - Kedir Lemma
Y1  - 2022/09/19
PY  - 2022
N1  - https://doi.org/10.11648/j.ajesa.20220902.12
DO  - 10.11648/j.ajesa.20220902.12
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 66
EP  - 72
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20220902.12
AB  - Studying the specific subject of opinion mining has been a popular research area as a means of overcoming the challenge of user-generated content on the web, which can be challenging to manually collect, comprehend, summarize, and analyze for decision-making. Even though there are three various levels at which opinion mining can be done, the detail and complexity of feature level opinion mining outweighs its disadvantages. The goal of this research is to provide sentiment mining and aspect-based opinion summaries of service reviews in Afaan Oromo for Oromia Radio and Television Organization (ORTO). 400 reviews in all were gathered and used for news-related purposes from ORTO. The model has five elements, including document inspection, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summary, as well as a bar chart to show aspect-based sentiment summation. Five different processes make up the model: document review, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summarization. A bar chart is also utilized to visually depict aspect-based opinion polarity. For positive classes, 90% precision and 87% recall are accomplished, while for negative classes, 87% precision and 89.7% recall are attained. The main issue identified in this study is that users tend to express their opinions in a context-based or indirect manner. They could express their negative feelings with pleasant words or the opposite. Therefore, more research is required before the algorithm will take context-based or semantic opinion mining into account.
VL  - 9
IS  - 2
ER  -

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