BULLETIN BOARD DATA MAPPING AND PRESENTATION
A computer-implemented method performed at a server system having one or more processors and memory, the method comprising receiving a set of curated documents comprising one or more documents identified as being relevant to a sector, analyzing the set of curated documents to determine one or more words and a count of each of the one or more words for all documents of the curated set of documents, further analyzing the set of curated documents, by analyzing one or more n-grams based on the one or more words, determining a first score based on a term frequency and a global document frequency of each of the one or more words of each of the one or more n-grams, determining a document vector based on averages of the first score, where the document vector comprises a perfect document for the sector, and storing the document vector in the data store.
1 . A computer-implemented method performed at a server system having one or more processors and memory, the method comprising:
receive a set of curated documents from a data store comprising one or more documents identified as being relevant to a sector;
analyze, by a processor, the set of curated documents to determine one or more words and a count of each of the one or more words for all documents of the curated set of documents;
further analyze the set of curated documents, by the processor, by analyzing one or more n-grams based on the one or more words;
determine, by the processor, a first score based on a term frequency and a global document frequency of each of the one or more words of each of the one or more n-grams;
determine, by the processor, a document vector based on averages of the first score, where the document vector comprises a perfect document for the sector; and
store the document vector in the data store.
2 . The method of claim 1 , further comprising:
receive a search query from a user to identify one or more relevant documents relevant to the sector from a set of unknown documents, wherein the set of unknown documents comprises documents stored on one or more other servers;
compare each of the documents in the set of unknown documents to the document vector and determine a second score for each of the documents in the set of unknown documents; and
display to the user, a list of the one or more relevant documents having the highest second score that are most relevant to the sector.
3 . The method of claim 2 , wherein determine the second score for each of the documents in the set of unknown documents comprises performing a cosine similarity analysis between each of the documents in the set of unknown documents and the document vector.
4 . The method of claim 1 , wherein the set of curated documents comprises less than 400 documents identified as being relevant to the sector by one or more users.
5 . The method of claim 1 , wherein the sector compnses at least one of mergers and acquisitions, financial updates, regulatory changes, legal issues, executive turnover, natural disasters, healthcare, education, schools, bankruptcy, or Hollywood news.
6 . The method of claim 1 , wherein the one or more n-grams comprise at least four words of the one or more words.
7 . A first system comprising:
a memory comprising instructions executable by one or more processors; and
the one or more processors coupled to the memory and operable to execute the instructions, the one or more processors being operable when executing the instructions to:
receive a set of curated documents from a data store comprising one or more documents identified as being relevant to a sector;
analyze, by a processor, the set of curated documents to determine one or more words and a count of each of the one or more words for all documents of the curated set of documents;
further analyze the set of curated documents, by the processor, by analyzing one or more n-grams based on the one or more words;
determine, by the processor, a first score based on a term frequency and a global document frequency of each of the one or more words of each of the one or more ngrams;
determine, by the processor, a document vector based on averages of the first score, where the document vector comprises a perfect document for the sector; and
store the document vector in the data store.
8 . The system of claim 7 , further operable to:
receive a search query from a user to identify one or more relevant documents relevant to the sector from a set of unknown documents, wherein the set of unknown documents comprises documents stored on one or more other servers;
compare each of the documents in the set of unknown documents to the document vector and determine a second score for each of the documents in the set of unknown documents; and
display to the user, a list of the one or more relevant documents having the highest second score that are most relevant to the sector.
9 . The system of claim 8 , wherein determine the second score for each of the documents in the set of unknown documents comprises performing a cosine similarity analysis between each of the documents in the set of unknown documents and the document vector.
10 . The system of claim 7 , wherein the set of curated documents comprises less than 400 documents identified as being relevant to the sector by one or more users.
11 . The system of claim 7 , wherein the sector compnses at least one of mergers and acquisitions, financial updates, regulatory changes, legal issues, executive turnover, natural disasters, healthcare, education, schools, bankruptcy, or Hollywood news.
12 . The system of claim 7 , wherein the one or more n-grams comprise at least four words of the one or more words.
13 . One or more computer-readable non-transitory storage media embodying software operable when executed by a first computer system to:
receive a set of curated documents from a data store comprising one or more documents identified as being relevant to a sector;
analyze, by a processor, the set of curated documents to determine one or more words and a count of each of the one or more words for all documents of the curated set of documents;
further analyze the set of curated documents, by the processor, by analyzing one or more n-grams based on the one or more words;
determine, by the processor, a first score based on a term frequency and a global document frequency of each of the one or more words of each of the one or more n-grams;
determine, by the processor, a document vector based on averages of the first score, where the document vector comprises a perfect document for the sector; and
store the document vector in the data store.
14 . The media of claim 13 , further operable to:
receive a search query from a user to identify one or more relevant documents relevant to the sector from a set of unknown documents, wherein the set of unknown documents comprises documents stored on one or more other servers;
compare each of the documents in the set of unknown documents to the document vector and determine a second score for each of the documents in the set of unknown documents; and
display to the user, a list of the one or more relevant documents having the highest second score that are most relevant to the sector.
15 . The media of claim 14 , wherein determine the second score for each of the documents in the set of unknown documents comprises performing a cosine similarity analysis between each of the documents in the set of unknown documents and the document vector.
16 . The media of claim 13 , wherein the set of curated documents comprises less than 400 documents identified as being relevant to the sector by one or more users.
17 . The media of claim 13 , wherein the sector compnses at least one of mergers and acquisitions, financial updates, regulatory changes, legal issues, executive turnover, natural disasters, healthcare, education, schools, bankruptcy, or Hollywood news.
18 . The media of claim 13 , wherein the one or more n-grams comprise at least four words of the one or more words.