Aspect-based Sentiment Analysis in the Field of Telecommunication Systems

Student: Anisimova Anna

Faculty: St. Petersburg School of Physics, Mathematics, and Computer Science

Educational Programme: Big Data Analysis for Business, Economy, and Society (Master)

Year of Graduation: 2020

This paper presents a solution to the problem of aspectual sentiment analysis based on user feedback provided by biggest russian telecom company MTS. The specificity of the data is such that reviews have a predominantly negative sentiment. To extract aspects from the data, the CRF model is used, it is trained by using active learning technology and expert markup. This approach showed high accuracy and speed of training. The resulting model can be used within the company to reduce costs in the work of the technical support department, as well as for business analytics and further decision-making.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses