III: Small: Better Sentiment Analysis through Forecasting

新兴的情感分析领域使用算法方法来识别和总结文本中表达的观点。机器学习和临时方法都是当代情感分析系统的基础，但由于获得足够广泛的通用情感训练/验证数据的成本和复杂性，提高准确性和召回率的进展已经放缓。最近的研究表明，通过将情感分析方法应用于面向新闻的文本流，可以成功地预测基本经济变量。这个项目颠覆了这种关系，使用这样的预测方法来提高一般面向实体的情感分析方法的精度和召回率。特别是，该项目为实体级情感分析提供了三管齐下的研究工作，重点是改进评估和算法，并将其应用于社会科学和预测。特别是：(1)开发一个完整的实体级、文本和语言独立的情感评估环境，既可以进一步发展Lydia系统，也可以向国际情感分析界发布。(2)在此环境的基础上，针对英语新闻、外语新闻流、博客、Twitter等社交媒体和历史文本语料库开发改进的情感检测方法。(3)最后，将改进的情感分析应用于社会科学中的各种挑战。这项研究承诺通过关注最薄弱的环节来大幅提高情感检测方法的精度和召回率：严格的、独立于领域、来源和语言的情感评估。除了自然语言处理（NLP）的改进之外，这还包括意见挖掘中的其他问题，包括文章聚类和重复检测、实体域上下文以及组合来自大量不同来源的意见。该研究项目开发的情感分析方法和数据预计将产生广泛的影响，因为其结果将直接适用于广泛的社会科学，包括社会学、经济学、政治学、媒体和传播学。这些技术将作为这些领域的教育和学术资源，使学生和研究人员能够对历史趋势和社会力量进行自己的初步研究。结果将通过项目网站（http://www.textmap.org/III）向社区公布。

The emerging field of sentiment analysis employs algorithmic methods to identify and summarize opinions expressed in text. Both machine learning and ad-hoc approaches lie at the foundations of contemporary sentiment analysis systems, but progress on improving both precision and recall has been slowed by the expense and complexity of obtaining sufficiently broad, general sentiment training/validation data.Recent work has established that fundamental economic variables can successfully be forecast by applying sentiment analysis methods to news-oriented text streams. This project turns this relation on its head, using such forecasting approaches to improve both the precision and recall of general entity-oriented sentiment analysis methods. In particular, this project provides a three-pronged research effort into entity-level sentiment analysis, focusing on improved assessment and algorithms, with applications to the social sciences and forecasting. In particular: (1) Developing a complete entity-level, text and language-independent sentiment evaluation environment, both to further the development of the Lydia system and for release to the international sentiment analysis community.(2) Building on this environment, to develop improved sentiment-detection methods for English news, foreign language news streams, social media such as blogs and Twitter, and historical text corpora.(3) Finally, applying improved sentiment analysis to a variety of challenges in the social sciences. This research promises to substantially improve both the precision and recall of sentiment detection methods, by focusing on the weakest link: rigorous yet domain-, source-, and language-independent assessment of sentiment. Beyond improvements in natural language processing (NLP), this includes other issues in opinion mining, including article clustering and duplicate detection, entity-domain context, and combining opinions from large numbers of distinct sources.The sentiment analysis methods and data developed under this research project are expected to have a broad impact, as the results will be directly applicable in a broad range of social sciences, including sociology, economics, political science, and media and communication studies. The techniques will serve as both an educational and scholarly resource in these fields, empowering students and researchers to conduct their own primary studies on historical trends and social forces. Results will be disseminated to the community through the project website (http://www.textmap.org/III).