Resource-intensive and data-intensive methods for robust fine-grained sentiment-analysis

我们提出了一个情感分析的研究建议，其中我们解决表达式级文本分析的缺点。这种细粒度的水平在以前的工作中没有得到太多的研究，即使对于实际应用，如意见问题回答或摘要，它是必不可少的。对于在这个任务中的主要类型的表达，即极性表达，如好的或可怕的，传达积极或消极的情绪，我们将重点放在未知词的问题。 我们计划研究使用形态分析的分解和合成的话。此外，我们将讨论极性强度的问题。我们计划系统地比较不同的自动排序方法，并与人类的评级。我们还将创建包含不同类型的价移词的词典。移位器对于上下文分类是必不可少的，因为它们修改甚至完全切换极性表达式所传达的极性。由于价移，到目前为止，主要是减少到处理常见的否定，这个任务需要一个更彻底的调查性质的转移。关于实体提取任务的表达水平的情感分析，即意见保持器和意见目标提取，我们的目标是创建新的词汇，可以作为骨干的基于规则的提取系统。这样的系统通常是相当独立于领域的，并且在没有标记文本数据的情况下很容易创建。为了解决上述任务，我们将使用资源密集型方法，即基于规则的方法，使用非常深的语义表示，以及数据密集型方法，即基于语料库的方法，也可以使用标准的NLP工具。我们将研究英语和德语的这些任务。由于大多数以前的自然语言处理研究都集中在前一种语言上，因此已经有一些复杂的资源可以用来研究更深层次的语言学方法。相比之下，后者则没有这些资源。因此，需要采用较浅的方法，通常是数据密集型方法。该项目的另一个贡献是，特别是德国，新的资源，如词汇资源和情感分析的处理工具，将被创建。在资源密集型和数据密集型方法的比较，我们还想回答的问题，哪种类型的表示是最适合不同的分类/提取任务，在细粒度的情感分析。在这种背景下，我们也将批判性地评估传统的基于词元的表征的适用性，并将其与其他潜在层次（如感觉层次）进行对比。最后，我们计划回顾已建立的评估方法，并检查它们是否足够透明，哪些现象分析系统处理得好，哪些不好。

We present a research proposal for sentiment analysis in which we address shortcomings on expression-level text analysis. This fine-grained level has not been examined much in previous work even though for practical applications, such as opinion question answering or summarization, it is essential.For the predominant type of expressions in this task, i.e. polar expressions such as nice or terrible that convey positive or negative sentiment, we will focus on the problem of unknown words. We plan to investigate the use of morphological analysis for both decomposing and synthesizing words. Moreover, we will address the issue of polar intensity. We plan to systematically compare different automatic ordering methods among each other and also with human ratings.We will also create lexicons that contain different types of valence shifters. Shifters are essential for contextual classification, as they modify or even fully switch the polarity conveyed by polar expressions. Since valence shifting, so far, has been mostly reduced to handling common negation, this task requires a more thorough investigation on the nature of shifting.With regard to the entity extraction tasks in expression-level sentiment analysis, i.e. opinion holder and opinion target extraction, we aim to create novel lexicons that can serve as the back-bone of rule-based extraction systems. Such systems are usually fairly domain-independent and easy to create in the absence of labeled textual data. In order to tackle the aforementioned tasks, we will employ both resource-intensive methods, i.e. rule-based methods that make use of very deep semantic representations, and data-intensive methods, i.e. corpus-based methods which may also employ standard NLP tools.We will examine these tasks for English and German. Since the majority of previous research in natural language processing focussed on the former language, there are already sophisticated resources available which allow investigations of deep(er) linguistic methods. By contrast, for the latter these resources are not available. Accordingly, shallower methods, typically data-intensive ones, need to be applied. One additional contribution of this project is that, in particular for German, new resources, such as lexical resources and processing tools for sentiment analysis, will be created.In connection with the comparison of resource-intensive and data-intensive methods, we also want to answer the question which type of representation is best suited for the different classification/extraction tasks in fine-grained sentiment analysis. In this context, we will also critically assess the suitability of traditional lemma-based representations and contrast them with other potential levels, such as the sense level.Finally, we plan to review established evaluation methods and examine whether they make sufficiently transparent which kinds of phenomena an analysis system handles well and which it does not.