Development of two data bases with comments in Bulgarian language and application of supervised learning approaches on them for comparative sentiment analysis. А brief overview

  • Daniela Ivanova Petrova Technical University- Varna
  • Violeta Bojikova Technical university of Varna
Keywords: Automatic Sentiment Analysis, opinion mining, supervised learning approaches, Bulgarian language

Abstract

The purpose of the current paper is to make an overview of the work done so far by the authors and make a summary of the results and reflections on the performed sentiment analysis on user comments in two different fields in Bulgarian language. As a starting point for the authors’ work is the development of two databases with users’ reviews and their preprocessing to become usable source of information for different types of analysis projects. As a result of the preprocessing is a revised Bulgarian language-driven algorithm for data preprocessing for Bulgarian language. The second part of the project is implemented into two steps: sentiment analysis using the supervised learning approaches developed for the two databases and a comparative sentiment analysis of the two databases, following their additional examination.

References

Dimitrova, T., Stefanova, V. (2018). The semantic classification of adjectives in the Bulgarian Wordnet: Towards a multiclass approach. Cognitive Studies | Etudes cognitives, 2018(18). Crossref

Hajmohammadi, M. S., Ibrahim, R., & Othman, Z. A. Opinion mining and sentiment analysis: a survey. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2(3c), 171-178. Crossref

Kapukaranov, B., Nakov, P., Fine-grained sentiment analysis for movie reviews in Bulgarian, Proceedings of Recent Advances in Natural Language Processing, p. 266-274, Hisar, Bulgaria, Sep.7-9 2015.
https://aclanthology.org/R15-1036.pdf

Nakov, P. (1998). BulStem: Design and Evaluation of Inflectional Stemmer for Bulgarian.
Retrieved from RG

Petrova, D. (2021) Automatic Sentiment Analysis on Hotel Reviews in Bulgarian – Basic Approaches and Results, IEMAICLOUD - London April 2021,

Petrova, D. (2021) Comparative assay on sentiment analysis on two databases in Bulgarian language, Interdisciplinary Conference on Mechanics, Computers and Electrics, Ankara, Turkey, 27-28 November 2021, ISBN: 978-625-409-707-2, to be published

Ramos, J.E. (2003). Using TF-IDF to determine word relevance in document queries. Tech. Rep., Department of Computer science. Rutgers University
Retrieved from  link

Стоянова Ив.”Автоматично разпознаване и тагиране на съставни лексикални единици в българския език“, BAS, Sofia, April 2012
Retrieved from https://ibl.bas.bg/wp-content/uploads/2014/10/IStoyanova-avtoreferat.pdf

Wankhade, M., Chandra, A.,Rao, S., Dara, S.,Kaushik, Baij. (2017). A sentiment analysis of food review using logistic regression. International Conference on Machine Learning and Computational Intelligence-2017, 2456-3307.
RG

Ye Q.,Z.Zhang, R.Law. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches, Expert Systems with Applications 36, 2009, p.6527-6535.
Crossref


Total number of hits on abstract = 50 times

Downloads for 2023

Download data is not yet available.
Published
2022-12-31
How to Cite
Petrova, D., & Bojikova, V. (2022, December 31). Development of two data bases with comments in Bulgarian language and application of supervised learning approaches on them for comparative sentiment analysis. А brief overview. ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA, 6(2), 57-62. https://doi.org/10.29114/ajtuv.vol6.iss2.261
Section
INFORMATION TECHNOLOGIES, COMMUNICATION AND COMPUTER EQUIPMENT
Bookmark and Share