Finding Mutual Benefit between Subjectivity Analysis and Information Extraction

"Subjectivity analysis" systems automatically identify and extract information relating to attitudes, opinions, and sentiments from text. As more and more people make their opinions available on the Internet and as people increasingly consult the Internet to ascertain other people's opinions about products, political issues, and so on, the demand for effective subjectivity analysis systems continues to grow. Information extraction systems, which automatically identify and extract factual information relating to events of interest, remain critically important in this day and age of increasingly vast amounts of text available online. In this work, we discover that these research areas are mutually beneficial. Information extraction techniques may be used to learn informative clues of subjectivity. Then, by bootstrapping from a lexicon of subjectivity clues, we can build a subjective-objective sentence classifier that does not require annotated data as input. This classifier may then be used to improve information extraction performance, on data which have not been annotated for subjectivity, by improving precision.