Note: To run YoGosling, you will need the twitter4j.properties, Java, Maven installed.
If you get into any problem or find any bug, please send an email to xeniaqian94@gmail.com
with a snippet of log where exception/error comes from, so that we can help/fix. :D
###Build with Maven
mvn clean package appassembler:assemble
YoGosling is a branch from Anserini[https://github.com/lintool/Anserini] project. Like Anserini, To run YoGosling, you
must save your Twitter API OAuth credentials in a file named twitter4j.properties in your current working YoGosling
root directory. See this page for more information about Twitter4j configurations.
The file should contain the following (replace the ********** instances with your information):
oauth.consumerKey=**********
oauth.consumerSecret=**********
oauth.accessToken=**********
oauth.accessTokenSecret=**********
###Index and search
sh target/appassembler/bin/TRECSearcher -groupid <groupid> -index <index_name> -host <host> -port <port>
-host, -port options are the url and port of the open RTS valuation broker. -groupid option is the groupid obtained
from RTS orgnizers. Details for getting groupids and conneting to RTS broker can be found at one of the discussions in the
mailing list.
###Connect evaluation broker REST(ful) API POST /register/system
https://github.com/YoGosling/Anserini/blob/master/src/main/java/io/anserini/rts/Registrar.java#L26
GET /topics/:clientid
POST /tweet/:topid/:tweetid/:clientid
To get rid of the whelming log info, there is a separate log to check whether YoGosling did the right thing: push seemingly "relevant" tweets! Under the root directory,
cd src/main/java/io/anserini/rts/scenarioLog
vi scenarioALog
Where you will probably see something like this,
Scenario A 24 Jun 2016 14:36:35 GMT 1466778995738 MB256 746351277148372992
Scenario A 24 Jun 2016 14:38:35 GMT 1466779115319 MB415 746351738509271040
Scenario A 24 Jun 2016 14:39:40 GMT 1466779180099 MB415 746352040503349249
Also for scenario B,
cd src/main/java/io/anserini/rts/scenarioLog
vi scenarioBLog
###Algorithm
YoGosling is a modified version of the best performing automatic system in TREC 2015. For algorithm details, please refer to the paper, Simple Dynamic Emission Strategies for Microblog Filtering
####Relevance Scoring Example: Star Wars
Document #298: { coins } ll 2016 Niue $2 1 oz. Proof Silver Star Wars Classics Series - Han Solo | GEM Proof (Original Mint ... link
Interest Profile: Star Wars
titleQuery: text:star text:wars
titleCoordSimilarity = 2/2 = 1.0
titleExpansionQuery: +(text:star^3.0 text:wars^3.0) #epoch:[1464847032 TO 1464847092]
titleExpansionSimilarity = 6.0 (as follows)
finalSimilarityScore = titleCoordSimilarity * titleExpansionSimilarity = 1.0 * 6.0 = 6.0
YoGosling log snippet
2016-06-02 13:58:12,592 INFO [Timer-2] rts.TRECScenarioRunnable (TRECScenarioRunnable.java:305) - 6.0 = sum of:
3.0 = weight(text:star^3.0 in 298) [TitleExpansionSimilarity], result of:
3.0 = score(doc=298,freq=1.0), product of:
3.0 = queryWeight, product of:
3.0 = boost
1.0 = idf(docFreq=1, maxDocs=445)
1.0 = queryNorm
1.0 = fieldWeight in 298, product of:
1.0 = tf(freq=1.0), with freq of:
1.0 = termFreq=1.0
1.0 = idf(docFreq=1, maxDocs=445)
1.0 = fieldNorm(doc=298)
3.0 = weight(text:wars^3.0 in 298) [TitleExpansionSimilarity], result of:
3.0 = score(doc=298,freq=1.0), product of:
3.0 = queryWeight, product of:
3.0 = boost
1.0 = idf(docFreq=1, maxDocs=445)
1.0 = queryNorm
1.0 = fieldWeight in 298, product of:
1.0 = tf(freq=1.0), with freq of:
1.0 = termFreq=1.0
1.0 = idf(docFreq=1, maxDocs=445)
1.0 = fieldNorm(doc=298)
2016-06-02 13:58:12,592 INFO [Timer-2] rts.TRECScenarioRunnable (TRECScenarioRunnable.java:306) - Multiplied by 1.0 Final score 6.0
2016-06-02 13:58:12,592 INFO [Timer-2] rts.TRECScenarioRunnable (TRECScenarioRunnable.java:308) - Raw text{ coins } ll 2016 Niue $2 1 oz. Proof Silver Star Wars Classics Series - Han Solo | GEM Proof (Original Mint ... https://t.co/6pQTdwW9Iw 2