SYSTEM OF AUTOMATIC DETERMINATION OF TEXT TONE

Igor Olenych, M. Prytula, O. Sinkevych, O. Khamar

Abstract


In the work, the methods of sentiment analysis of the text are considered. The system of emotion detection of Ukrainian-language texts based on dictionaries and rules is proposed. The developed software downloads text information in various formats and carries out tokenization and lemmatization procedures using the Python TokenizeUKand pymorphy2 libraries. As a result, an array of words in the basic grammatical form using for determining the tone of the text is formed. The obtained word base was analyzed using a tonal dictionary of the Ukrainian language. A dictionary of synonyms was used to expand the vocabulary. If there is no word in the tonal dictionary, the tonality value of its nearest synonym is used for further calculations.

Computer analysis of textual information was performed in five emotional categories namely: very positive, positive, neutral, negative, and very negative tone of words. To increase the accuracy and validity of sentiment analysis, coefficients were used that take into account the various emotional load of words of different speech parts and their dissimilar impact on the overall assessment of the text tone. The proposed system of sentiment analysis assumes a greater emotional influence of adjectives compared to verbs and nouns. Since the tone of textual information and the expression of human emotions are subjective factors, the means of fuzzy modeling are used for sentiment analysis of texts. This approach makes it possible to take into account the contribution of all emotional categories in the final evaluation of the text. As a result of an aggregation of normalized data on different emotion categories and defuzzification by the method of the center of gravity for one-element sets, a quantitative estimate of the emotional tone of texts was obtained.

The developed system of sentiment analysis was tested on Ukrainian-language texts from different sources and different emotional tones. The use of additional tools provides the analysis of more words in the text and the degree of their emotional tone, which leads to more correct detection of the tone of the whole text.

Key words: computer text analysis, sentiment analysis, tokenization, tonality dictionary, fuzzy modeling.


References


Gallagher C. The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying / C. Gallagher, E. Furey, K. Curran // International Journal of Data Warehousing and Mining. – 2019. – Vol. 15(4). – P. 21–47.

Drus Z. Sentiment Analysis in Social Media and Its Application: Systematic Literature Review / Z. Drus, H. Khalid // Procedia Computer Science. – 2019. – Vol. 161. – P. 707–714.

Poecze F. Social Media Metrics and Sentiment Analysis to Evaluate the Effectiveness of Social Media Posts / F. Poecze, C. Ebster, C. Strauss // Procedia Computer Science. – 2018. – Vol. 130. – P. 660–666.

Gursoy U.T. Social Media Mining and Sentiment Analysis for Brand Management / U.T. Gursoy, D. Bulut, C. Yigit // Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology. – 2017. – Vol. 3. – P. 497–551.

Schumaker R.P. Evaluating sentiment in financial news articles / R.P. Schumaker, Y. Zhang, C.N. Huang, H. Chen // Decision Support Systems. – 2012. – Vol. 53, No 3. – P. 458–464.

Hao J. Social Media Content and Sentiment Analysis on Consumer Security Breaches / J. Hao, H. Dai // Journal of Financial Crime. – 2016. – Vol. 23, No 4. – P. 855–869.

Mansour S. Social Media Analysis of User’s Responses to terrorism using sentiment analysis and text mining / S. Mansour // Procedia Computer Science. – 2018. – Vol. 140. – P. 95–103.

Feldman R. Techniques and Applications for Sentiment Analysis / Ronen Feldman // Communications of the ACM. – 2013. – Vol. 56, No 4. – P. 82–89.

D'Andrea A. Approaches, Tools and Applications for Sentiment Analysis Implementation / A. D'Andrea, F. Ferri, P. Grifoni, T. Guzzo // International Journal of Computer Applications. – 2015. – Vol. 125, No 3. – P. 26–33.

Howellsa K. Applying fuzzy logic for sentiment analysis of social media network data in marketing / K. Howellsa, A. Ertugan // Proc. Comp. Sci. – 2017. – Vol. 120. – P. 664–670.

Jain P. Aspect Based Sentiment Analysis by Fuzzy Logic / P. Jain, A. Srivastava, V. Singh, B. Hazela // International Journal of Current Engineering and Technology. – 2019. – Vol. 9, No 2. – P. 243–248.

Liu H. Fuzzy Rule Based Systems for Interpretable Sentiment Analysis / H. Liu, M. Cocea // International Conference on Advanced Computational Intelligence. – 2017. – P. 129–136.

Pang B. Opinion Mining and Sentiment Analysis / B. Pang, L. Lee // Foundations and Trends in Information Retrieval. – 2008. – Vol. 2. – P. 1–135.

Khurshid A. Affective Computing and Sentiment Analysis: Metaphor, Ontology, Affect and Terminology / A. Khurshid – Berlin: Springer Science & Business Media, 2011 – 164 p.

Jain U. A Review on the Emotion Detection from Text using Machine Learning Techniques / U. Jain, A. Sandhu // International Journal of Current Engineering and Technology. – 2015. – Vol.5, No.4. – P. 2645–2650.

Chopra F.K. Sentiment Analyzing by Dictionary based Approach / F.K. Chopra, R. Bhatia // International Journal of Computer Applications. – 2016. – Vol. 152, No.5. – P. 32–34.

Thelwall M. Sentiment strength detection in short informal text / M. Thelwall, K. Buckley, G. Paltoglou, A. Kappas, D. Cai // Journal of the American Society for Information Science and Technology. – 2010. – No. 61. – P. 2544–2558.

Denecke K. Using SentiWordNet for Multilingual Sentiment Analysis / K. Denecke // International Conference on Data Engineering Workshops. – 2008. – Р. 507–512.

Dang Y. A lexicon enhanced method for sentiment classification: An experiment on online product reviews / Y. Dang, Y. Zhang, H. Chen // IEEE Intelligent Systems. – 2010. – Vol. 25, No.4. – P. 46–53.

Український тональний словник [Електронний ресурс]. – Режим доступу: https://github.com/lang-uk/tone-dict-uk/blob/master/tone-dict-uk.tsv

Український тональний словник [Електронний ресурс]. – Режим доступу: https://github.com/lang-uk/tone-dict-uk/blob/master/tone-dict-uk-manual.tsv

Takagi Т. Fuzzy Identification of Systems and Its Applications to Modeling and Control / Т. Takagi, M. Sugeno // IEEE Transactions on Systems, Man and Cybernetics. – 1985. – Vol. 15. – P. 116–132.




DOI: http://dx.doi.org/10.30970/eli.15.2

Refbacks

  • There are currently no refbacks.