Target-level sentiment analysis (TLSA) is a classification task to extract sentiments from targets in text. In this paper, we propose target-dependent convolutional neural network (TCNN) tailored to ...the task of TLSA. The TCNN leverages the distance information between the target word and its neighboring words to learn the importance of each word to the target. Experimental results show that the TCNN achieves state-of-the-art performance on both single- and multi-target datasets. Qualitative evaluations were conducted to demonstrate the limitations of previous TLSA methods and also to verify that distance information is crucial for TLSA. Furthermore, by exploiting a convolutional neural network (CNN), the TCNN trains six times faster per epoch than other baselines based on recurrent neural networks.
The rapid growth of social media, news sites, and blogs increases the opportunity to express and share an opinion on the Internet. Researchers from different fields take advantage of nearly limitless ...data. Thus, in the past decade, opinion mining or sentiment analysis has become an important research discipline. In this paper, we focus on the target-level sentiment analysis, wherein the task is to predict the sentiment concerning specific (multiple) entities that appear as coreference mentions throughout the document. We created a new annotated dataset of Slovene news articles, additionally annotated with named entities and coreferences that are the basis for the proposed task. Using entity-document representation, we compared the task with the traditional sentiment analysis, evaluating traditional machine learning and deep neural network approaches. According to existing approaches, the proposed task represents a challenging problem. The results show that we can achieve the best results using a customised BERT adapter (a minor improvement over a standard text-classification adapter). We outperformed existing aspect-based state-of-the-art approaches by 13%, reaching up to 77% accuracy and a 73% F1 score.
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