Language Models Application in Sentiment Attitude Extraction Task

Large text can convey various forms of sentiment information including the author’s position, positive or negative effects of some events, attitudes of mentioned entities towards to each other. In this paper, we experiment with BERT based language models for extracting sentiment attitudes between named entities. Given a mass media article and list of mentioned named entities, the task is to ex tract positive or negative attitudes between them. Efficiency of language model methods depends on the amount of training data. To enrich training data, we adopt distant supervision method, which provide automatic annotation of unlabeled texts using an additional lexical resource. The proposed approach is subdivided into two stages FRAME-BASED: (1) sentiment pairs list completion (PAIR-BASED), (2) document annotations using PAIR-BASED and FRAME-BASED factors. Being applied towards a large news collection, the method generates RuAttitudes2017 automatically annotated collection. We evaluate the approach on RuSentRel-1.0, consisted of mass media articles written in Russian. Adopting RuAttitudes2017 in the training process results in 10-13% quality improvement by F1-measure over supervised learning and by 25% over the top neural network based model results.

SENTIMENT FRAMES FOR ATTITUDE EXTRACTION IN RUSSIAN

Texts can convey several types of inter-related information concerning opinions and attitudes. Such information includes the author’s attitude towards mentioned entities, attitudes of the entities towards each other, positive and negative effects on the entities in the described situations. In this paper, we described the lexicon RuSentiFrames for Russian, where predicate words and expressions are collected and linked to so-called sentiment frames conveying several types of presupposed information on attitudes and effects. We applied the created frames in the task of extracting attitudes from a large news collection.

Using Convolutional Neural Networks for Sentiment Attitude Extraction from Analytical Texts

In this paper we present an application of the specific neural network model for sentiment attitude extraction without handcrafted NLP features implementation. Given a mass-media article with the list of named entities mentioned in it, the task is to extract sentiment relations between these entities. We considered this problem for the whole documents as a three-class machine learning task. The modified architecture of the Convolutional Neural Networks were used and called as Piecewise Convolutional Neural Network (PCNN). The latter exploits positions of named entities in text to emphasize aspects for inner and outer contexts of relation between entities. For the experiments, the RuSentRel corpus was used, it contains Russian analytical texts in the domain of international relations.