Abstract: A sales prediction system predicts sales from online public discussions. The system utilizes manually or automatically formulated predicates to capture subsets of postings in online public discussions. The system predicts spikes in sales rank based on online chatter. The system comprises automated algorithms that predict spikes in sales rank given a time series of counts of online discussions such as blog postings. The system utilizes a stateless model of customer behavior based on a series of states of excitation that are increasingly likely to lead to a purchase decision. The stateless model of customer behavior yields a predictor of sales rank spikes that is significantly more accurate than conventional techniques operating on sales rank data alone.

Abstract: A method and system for generating threads of documents from a collection C of documents containing terms. Each document of C has a timestamp and an associated timestamp index. The timestamp indexes are ordered in accordance with an ordering of the associated timestamps. A relevance graph G generated from C is an acyclic directed graph. Each node of G denotes a document of C. Each edge of G connects a pair of directed nodes pointing from a node having an earlier timestamp to a node having a later timestamp. At least one thread of G is determined by executing a matching-based algorithm or a dynamic programming algorithm. Each thread is a path through G originating at a first node and terminating at a second node and including one or more contiguous edges from the first node to the second node. The at least one thread is outputted.

Abstract: A topic segmenting system segments a topic into chatter and subtopics. The system decomposes a conversation into topics, producing a time-based structure for topics and subtopics in the conversation. The system extracts a large number of topics at all levels of granularity. Some of the topics extracted correspond to broad topics and some correspond to “spiky” topics or subtopics. The system comprises a process for automatically detecting spiky regions of a topic. For each possible broad topic, the present system finds regions where coverage of the broad topic overlaps significantly with the spiky region of another topic. The system then removes the spiky subtopic from the conversation. Processing is repeated until all discernable topics have been identified and removed from the conversation, yielding random topics of little duration or intensity.