Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.
翻译:感官预测仍然是包括心理学、神经科学和计算机科学在内的各种研究领域一项具有挑战性和未解决的任务,这源于其高度主观性和有限的投入来源,能够有效捕捉到实际情绪。如果只有基于文本的投入,这甚至更具有挑战性。与此同时,深层次学习的兴起和前所未有的大量数据为人工智能提供令人印象深刻的准确预测,甚至人类层面的推理铺平了道路。从中,我们建议了一个基于覆盖面的情绪和次感应提取系统,对输入文本的范围进行估计,并循环地将这些信息反馈到网络。预测的次感由表达情绪的辅助信息组成。这是促成生动和震动情绪传递(在本文范围内)和其他自然语言处理任务(如文本拼凑和“A”等)的重要基础。我们的方法大大超越了次感应预测中的最新方法(即平均 Jaccard分数从0.72到0.89),为了评估,我们设计了由24种公共分析研究组成的严格实验。最后,我们所学到的教训可追溯到社区。