面向实时财经信息的领域情感歧义搭配词典构建研究

Domain Ambiguous Collocation Dictionary for Real-Time Financial Sentimental Analysis

【目的】针对由于忽略歧义词的动态极性而导致情感分析有误的问题,有效识别具有经济学特征的情感歧义词并提取其搭配词,解决该领域歧义词适配性问题。【方法】以动态财经新闻信息为研究对象,计算短语中词汇正负情感值以识别提取歧义种子词,通过关联规则、点互信息等算法挖掘其强相关搭配词,标注搭配词对情感极性后构建歧义搭配词典,从动态维度对实时更新的新闻文本进行情感挖掘测评。【结果】实证结果表明,加入歧义搭配词典后对财经信息文本情感分析的准确率为89.62%,召回率为87.52%,F1值为88.57%,较未加入歧义搭配词典分别提高5.79、15.89和10.84个百分点。【局限】在利用情感歧义搭配词典进行文本情感挖掘过程中,存在设置种子词与其搭配词检索字符间隔较远而未被有效识别的情况。【结论】本文构建的歧义搭配词典有效扩充了经济学领域情感词典,在细粒度和深度上对领域情感词典进行完善及优化,显著提升了领域文本情感挖掘的准确性。

[Objective]This study tries to address the problem of inaccurate sentiment analysis due to ignoring the dynamic polarity in ambiguous words.It aims to effectively identify sentiment-ambiguous words with economic characteristics and their collocations.[Methods]The study takes dynamic financial news information as the research object.First,we calculated the positive and negative sentiment scores of words in phrases to extract ambiguous seed words.Then,we retrieved their strongly related collocations with algorithms such as association rules and PMI.Third,we labeled the sentiment polarity of collocation pairs to build an ambiguous collocation lexicon.Finally,we measured the performance of sentiment mining on real-time updated news texts from a dynamic perspective.[Results]The accuracy,recall,and F-value of the sentiment analysis of the financial information text were 89.62%,87.52%,and 88.57%,respectively,which were 5.79%,15.89%,and 10.84%higher than the traditional models.[Limitations]Some collocation words cannot be identified due to their significant distance from the seed words.[Conclusions]The ambiguous collocation dictionary constructed in this paper effectively expands the sentiment lexicon in economics.It optimizes the lexicon in granularity and depth,significantly improving sentiment analysis accuracy.