Aspect based sentiment analysis (ABSA), exploring sentim- ent polarity of aspect-given sentence, has drawn widespread applications in social media and public opinion. Previously researches typically derive aspect-independent representation by sentence feature generation only depending on text data. In this paper, we propose a Position-Guided Contributive Distribution (PGCD) unit. It achieves a position-dependent contributive pattern and generates aspect-related statement feature for ABSA task. Quoted from Shapley Value, PGCD can gain position-guided contextual contribution and enhance the aspect-based representation. Furthermore, the unit can be used for improving effects on multimodal ABSA task, whose datasets restructured by ourselves. Extensive experiments on both text and text-audio level using dataset (SemEval) show that by applying the proposed unit, the mainstream models advance performance in accuracy and F1 score.
翻译:基于视觉的情绪分析(ABSA),探索侧写句子的寄存点对极性,在社交媒体和公众舆论中广泛应用,以往的研究通常只根据文本数据,按刑期生成外在代表特征;在本文件中,我们提议建立一个基于位置的辅助性贡献分布(PGCD)单位,实现基于位置的贡献模式,并为ABSA任务生成与侧有关的语句特征;从Shapley 价值引证,PGCD可以取得定位引导背景贡献,加强侧写面代表;此外,该单位可用于改善多式ABSA任务的效果,该任务的数据由我们自己重组;利用数据集(SemEval)对文本和文本-音频水平进行广泛实验,表明通过应用拟议单元,主流模型可以提高准确性和F1分的性能。