| 摘要 |
An automatic language-processing system uses a human-curated lexicon to associate words and word groups with broad sentiments such as fear or anger, and topics such as accounting fraud or earnings projections. Grammar processing further characterizes the sentiments or topics with logical ("is" or "is not"), conditional (probability), temporal (past, present, future), quantitative (larger/smaller, higher/lower, etc.), and speaker identification ("I" or "He" or "Alan Greenspan") measures. Information about the characterized sentiments and topics found in electronic messages is stored in a database for further analysis, display, and use in automatic trading systems.
|
