Journal sections
Archive and statistics
Log in

Печатный вестник PRINTED
Издательство МГОУ Publishing house the

Our address: 105005, Moscow, Radio street,10a, office 98.

+7 (495) 780-09-42 add. 1740,
+7 (495) 723-56-31


Work schedule: Monday to Thursday from 10-00 to 17-30,

Friday from 10:00 to 16-00,

lunch break from 13:00 to 14-00.



BK Facebook Telegram Twitter Instagram

Bulletin of the MRSU / Section "Philological sciences" / 2019 № 2.


T.A. Semina

AUTHOR’S OPINION MINING USING INVERSE DOCUMENT FREQUENCY. In: Bulletin of the Moscow Region State University (electronic journal), 2019, no. 2, pp. 171-179.


UDC Index: 81-114.2

Date of publication: 04.06.2019 Page: 171 - 179

The full text of the article

Downloads count Downloads count: 70


The purpose of the article is to identify patterns for author’s opinion mining in articles relating to the genre of information journalism, that imposes a restriction on the expression of judgments and opinions. A corpus of political articles has been assembled by the author and the metric from information retrieval called inverse document frequency has been counted for every token in the collection. The applicability of this criterion has been shown for the search of colloquial language which is an indicator of the presence of the author’s opinion. In conclusion, the directions of further research of this problem are formulated and the question of identifying the author’s opinion through the inverse document frequency in other texts is considered.

Key words

sentiment analysis, information retrieval, inverse document frequency, term frequency, opinion

List of references

1. Alekseev A. A., Kugurakova V. V., Ivanov D. S. [The identification of a psychological portrait based on the definition of key messages for anthropomorphic social agent]. In: Elektronnye biblioteki [Electronic Libraries], 2016, vol. 19, no. 3, pp. 149–165.
2. Akhrenova N. A. [Internet linguistics: a new paradigm in the description of the language of the Internet]. In: Vestnik Moskovskogo gosudarstvennogo oblastnogo universiteta. Seriya: Lingvistika [Bulletin of the Moscow Region State University. Series: Linguistics], 2016, no. 3, pp. 8–14.
3. Geiko N. R., Sirivlya M. A. [Implicit evaluations in political discourse]. In: Vestnik Bryanskogo gosudarstvennogo universiteta [Bulletin of Bryansk State University], 2016, no. 2 (28), pp. 164–166.
4. Mozharova V. A., Lukashevich N. V. [Examination of the indications for the extraction of named entities from texts in Russian]. In: Nauchno-tekhnicheskaya informatsiya. Seriya 2: Informatsionnye protsessy i sistemy [Scientific and technical information. Series 2: Information Processes and Systems], 2017, no. 5, pp. 14–21.
5. Pazel’skaya A. G., Solov’ev A. N. [The method of definition of emotions in Russian texts]. In: Komp’yuternaya lingvistika i intellektual’nye tekhnologii: po materialam ezhegodnoi Mezhdunarodnoi konferentsii «Dialog», Bekasovo, 25–29 maya 2011 g. Vyp. 10 (17) [Computational linguistics and intellectual technologies: based on the materials of the annual International Conference “Dialogue”, Bekasovo, May 25–29, 2011. Iss. 10 (17)]. Мoscow, Publishing house of the Russian State University for the Humanities Publ., 2011, pp. 510–522.
6. Semina T. A. [The dichotomy of subjectivity vs. objectivity and tone the relevance in the task of sentiment analysis]. In: Vestnik Moskovskogo gosudarstvennogo oblastnogo universiteta. Seriya: Lingvistika [Bulletin of the Moscow Region State University. Series: Linguistics], 2018, no. 1, pp. 38–45.
7. Chen K., Zhang Z., Long J., Zhang H. Turning from TF-IDF to TF-IGM for term weighting in text classification. In: Expert Systems With Applications, 2016, no. 66, pp. 245–260.
8. Choi Y., Wiebe J., Mihalcea R. Coarse-grained +/– Effect Word Sense Disambiguation for Implicit Sentiment Analysis. In: The IEEE Transactions on Affective Computing, 2017, vol. 8, no. 4, рр. 471–479.
9. Deng L., Wiebe J. MPQA 3.0: An Entity/Event-Level Sentiment Corpus. In: Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL. Denver, Colorado, 2015, pp. 1323–1328.
10. Peng M., Zhang Q., Jiang Y. G. Cross-Domain Sentiment Classification with Target Domain Specific Information. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Melbourne, 2018, pp. 2505–2513.
11. Wiebe J. M. Tracking Point of View in Narrative. In: Computational Linguistics, 1994, vol. 20, no. 2, pp. 233–287.

Лицензия Creative Commons