Proceedings of ISP RAS


Modern Approaches to Aspect-Based Sentiment Analysis

I. Andrianov (ISP RAS, Moscow), V. Mayorov(ISP RAS, Moscow), D. Turdakov(ISP RAS, Moscow; MSU, Moscow; HSE, Moscow)

Abstract

The paper presents a survey of methods solving the actual task of aspect-based sentiment analysis. Solutions for this task were proposed at multiple natural language processing conferences. Organizers of these conferences proposed evaluation platforms for methods for aspect-based sentiment analysis. This paper describes methods proposed by participants of two international evaluation platforms: SemEval-2015 focusing on English texts and SentiRuEval-2015 focusing on Russian texts.
SemEval-2015 organizers provided participants with the task 12.2 for English language and two domains: restaurants and laptops. The task was split to multiple subtasks two of which are described in this paper: opinion target expression (both explicit and implicit ones) extraction and sentiment polarity detection. Described methods for opinion target expression can be split to the following categories: sequence labeling, domain-specific terminology extraction and unsupervised learning. Methods for sentiment polarity detection varied from classification-based to unsupervised learning.
SentiRuEval-2015 organizers provided participants with the tasks A, B and C for Russian language and two domains: restaurants and automobiles. Task A was devoted to explicit aspect term extraction, task B – to explicit, implicit and factual aspect term extraction. Sentiment polarity detection was subject of the Task C. Described methods for aspect term extraction can be classified as following: sequence labeling, word2vec-based and neural network-based. Methods for sentiment polarity detection varied from word2vec-based to neural network-based.

Keywords

sentiment analysis; aspect extraction; text processing; machine learning

Edition

Proceedings of the Institute for System Programming, vol. 27, issue 5, 2015, pp. 5-22.

ISSN 2220-6426 (Online), ISSN 2079-8156 (Print).

DOI: 10.15514/ISPRAS-2015-27(5)-1

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