Why deep parsing rules instead of deep learning model for sentiment analysis?


(1)    Learning does not work in short messages as short messages do not have enough data points (or keyword density) to support the statistical model trained by machine learning.  Social media is dominated by short messages.

(2)    With long messages, learning can do a fairly good job in coarse-grained sentiment classification of thumbs-up and thumbs-down, but it is not good at decoding the fine-grained sentiment analysis to answer why people like or dislike a topic or brand.  Such fine-grained insights are much more actionable and valuable than the simple classification of thumbs-up and thumbs-down.

We have experimented with and compared  both approaches to validate the above conclusions.  That is why we use deep parsing rules instead of a deep learning model to reach the industry-leading data quality we have for sentiment analysis.

We do use deep learning for other tasks such as logo and image processing.  But for sentiment analysis and information extraction from text, especially in processing social media, the deep parsing approach is a clear leader in data quality.



The mainstream sentiment approach simply breaks in front of social media

Coarse-grained vs. fine-grained sentiment analysis

Deep parsing is the key to natural language understanding 

Automated survey based on social media

Overview of Natural Language Processing

Dr. Wei Li’s English Blog on NLP




立委博士,自然语言处理(NLP)资深架构师,讯飞AI研究院副院长。前 Principle Scientist, jd-valley, 主攻深度解析和知识图谱及其应用。Netbase前首席科学家,期间指挥研发了18种语言的理解和应用系统。特别是汉语和英语,具有世界一流的解析(parsing)精度,并且做到鲁棒、线速,scale up to 大数据,语义落地到数据挖掘和问答产品。Cymfony前研发副总,曾荣获第一届问答系统第一名(TREC-8 QA Track),并赢得17个美国国防部的信息抽取项目(PI for 17 SBIRs)。立委NLP工作的应用方向包括大数据舆情挖掘、客户情报、信息抽取、知识图谱、问答系统、智能助理、语义搜索等等。