A computational treatment of negation and speculation in natural language

情感分析（也称为意见挖掘）利用计算语言学和文本分析的方法来确定文本（标题，新闻文章，电影评论，电子邮件消息，博客或Twitter帖子）主要是客观的还是主观的，如果是后者，无论是积极的还是消极的。在过去的几年里，在如何表现和分析情感和观点方面已经取得了很大的进展，但是否定、推测和观点的相互作用仍然是一个难以捉摸的任务。例如，在句子“Some may say that it's not a good movie”中，“good”中明显的积极观点被同一短语中的“not”否定，但又被“some may say”所传达的推测所缓和。事实上，这句话似乎暗示作者实际上对这部电影有好感。在当前的情感分析系统中，通常不会检测到这种含义，因为投机很少被解决。 我的研究计划的长期目标是开发自然语言的计算模型，评估和实施这些模型，使它们可以应用于不同类型的文本。短期目标是找到提取否定和猜测的方法，并发现它们在表达情感和意见中的作用。 我的研究将通过使用机器学习方法来自动发现自然语言中的否定和推测现象来解决这些问题。机器学习方法涉及分类器，这些分类器学习哪些特征和线索是特定现象的可靠指标，在这种情况下是否定和推测。其结果将是一个软件模块，它将文本作为输入，并准确地识别否定和猜测所在。这个模块将被集成到我的实验室开发的情感分析系统中，语义取向计算器（SO-CAL）。 本研究的预期成果是一套方法，用于识别否定和猜测，一个模块，实现这些方法，和一个改进的SO-CAL，这将能够确定高精度的语言表达的情感。该软件将提供给科学界。潜在的应用包括提取关于产品，政治家和时事的意见，这将是社交媒体分析公司，决策者，记者，版主和公众，特别是那些经常使用社交媒体的人感兴趣的。除了对情感分析领域的贡献外，否定和推测的检测在许多计算语言学任务中也很重要，包括文本摘要和信息检索。

Sentiment analysis (also known as opinion mining) makes use of methods in computational linguistics and text analysis to determine whether a text (headline, news article, movie review, e-mail message, blog or Twitter post) is mostly objective or subjective and, if the latter, whether positive or negative towards its subject matter. Much progress has been made in the last few years in how to represent and analyze sentiment and opinion, but the interaction of negation, speculation and opinion remains an elusive task. For instance, in the sentence 'Some may say that it’s not a good movie', the apparent positive opinion in 'good' is negated by 'not' in the same phrase, but tempered again by the speculation conveyed by 'some may say'. This sentence, in fact, seems to imply that the author actually has a favourable opinion of the film. The implication is not typically detected in current sentiment analysis systems, because speculation is rarely addressed. The long-term objectives of my research program are to develop computational models of natural language, evaluating and implementing such models so that they can be applied to different types of texts. The short-term objectives are to find methods to extract negation and speculation, and to discover their role in the expression of sentiment and opinion. My research will address the problem of negation and speculation by using machine learning methods to automatically find these phenomena in natural language. Machine learning methods involve classifiers which learn which features and cues are reliable indicators of a particular phenomenon, in this case negation and speculation. The result will be a software module which takes a text as input, and accurately identifies where negation and speculation lie. This module will be integrated into the sentiment analysis system developed within my lab, the Semantic Orientation Calculator (SO-CAL). The anticipated outcomes of this research are a set of methods for identifying negation and speculation, a module that implements those methods, and an improved SO-CAL, which will be able to determine with high accuracy the sentiment expressed in language. The software will be made available to the scientific community. Potential applications include extracting opinion about products, politicians and current events, which will be of interest to companies in social media analysis, decision-makers, journalists, moderators, and the general public, in particular those who use social media regularly. In addition to the contribution to the field of sentiment analysis, detection of negation and speculation is important in many computational linguistics tasks, including text summarization and information retrieval.