Skip to content

thanos/jinfinigon

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

39 Commits
.idea
.idea
 
 
dist/jinfinigon_jar
dist/jinfinigon_jar
 
 
src
src
 
 
.gitignore
.gitignore
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 

Repository files navigation

jinfinigon

A java api for the Infinigon's Social Analytical service This api offers three iterable classes Timeline, Tweets, and Snaphots.

REQUIRED LIBRARIES

The only prequisit is that the API uses by default json-simple-1.1.1.jar and jinfinigon.jar. Down load both jars and put it somewhere in your CLASS_PATH.

Connecting.

All the iterators take an optional Proxy object.

Token

For anonymous users the API is throttled. This allows you to either test against our system or create pages that are fairly static but calling these too frequently will get you an error such as: callback({"detail": "Request was throttled.Expected available in 85250 seconds."}); For any serious use you will need to use our simple token-based HTTP Authentication scheme. By default the API checks for an environment variable INFINIGON_TOKEN. To use your own token strategy just override getToken() of com.infinigongroup.api.InfinigonIterable.

Using a Proxy:

Proxy proxy = new Proxy(Proxy.Type.HTTP, new InetSocketAddress("10.38.89.25", 8080));
TimeSeries timeline = new Timeline("AAPL", Timeline.M, proxy);
for (Object timepoint : timeline) {
	System.out.println(timepoint);
}

com.infinigongroup.api.Timeline

The iterable Timeline class yields timepoints of data for a given stream that enable you to build timeline charts for individual streams.

Timeline Data

{	"date":"2015-08-19 00:45Z",
	"sentiment":0.0,
	"tweets":2
}

Timeline Parameters

stream & resolution

The constructor requires stream and resolution where:

  • stream - is the stream id such as AAPL or FDA
  • resolution - Data is aggregated by minute, hour or day (for more see Resolutions ) so you can use:
code resolution
TimeSeries.M for minutes
TimeSeries.H for hours
TimeSeries.d for days
start & stop

Optionally you can give a date and time range by setting start and stop. stop parameter always defaults to now, while start's default value depends of the resolution on given.

Format Default Start Value
TimeSeries.M 24 hours ago
TimeSeries.H 7 days ago
TimeSeries.d 30 days ago

You can specify the dates using java.utils.Date or you can also use a String in many formats (see [Date Formats] (http://realtime.infinigongroup.com/api/docs/#data_dates)). All times are by default UTC so you must be explicit and add the timezone. You can test your date and time values using:

Time Delta

For the start parameter you can also give a time delta, specifying a period of time before the given (or default) stop date.

Period Code | Period | Example | Description --- | --- | --- | --- | --- M | minutes | "30M" | starting thirty minutes ago H | hours | "8H" | starting eight hours ago d | days |"5d" | starting five days ago w | weeks | "2w" | starting fortnight ago m | months | "3m" | starting on the same date 3 months ago y | years | "1y" | starting a year ago

Timeline Examples

Reading minute timepoints for AAPL
TimeSeries timeline = new Timeline("AAPL", TimeSeries.M, proxy);
int i =0;
for (Object timepoint : timeline) {
	System.out.print(i++ + ". ");
	System.out.println(timepoint);
}
0. {"date":"2015-08-19 16:35Z","sentiment":0.67,"tweets":18}
1. {"date":"2015-08-19 16:36Z","sentiment":0.64,"tweets":39}
2. {"date":"2015-08-19 16:37Z","sentiment":0.67,"tweets":21}
3. {"date":"2015-08-19 16:38Z","sentiment":0.73,"tweets":37}
4. {"date":"2015-08-19 16:39Z","sentiment":0.63,"tweets":52}
5. {"date":"2015-08-19 16:40Z","sentiment":0.61,"tweets":28}
6. {"date":"2015-08-19 16:41Z","sentiment":0.66,"tweets":32}
7. {"date":"2015-08-19 16:42Z","sentiment":0.65,"tweets":37}
8. {"date":"2015-08-19 16:43Z","sentiment":0.63,"tweets":30}
9. {"date":"2015-08-19 16:44Z","sentiment":0.7,"tweets":44}
Reading hour of timepoints for AAPL
TimeSeries timeline = new Timeline("AAPL", TimeSeries.H, proxy);
int i =0;
for (Object timepoint : timeline) {
	System.out.print(i++ + ". ");
	System.out.println(timepoint);
}
0. {"date":"2015-08-10 17:00Z","sentiment":0.65,"tweets":2193}
1. {"date":"2015-08-10 18:00Z","sentiment":0.64,"tweets":2326}
2. {"date":"2015-08-10 19:00Z","sentiment":0.66,"tweets":2408}
3. {"date":"2015-08-10 20:00Z","sentiment":0.65,"tweets":2209}
4. {"date":"2015-08-10 21:00Z","sentiment":0.62,"tweets":1974}
5. {"date":"2015-08-10 22:00Z","sentiment":0.63,"tweets":1857}
6. {"date":"2015-08-10 23:00Z","sentiment":0.64,"tweets":1762}
7. {"date":"2015-08-11 00:00Z","sentiment":0.63,"tweets":1671}
8. {"date":"2015-08-11 01:00Z","sentiment":0.63,"tweets":1665}
9. {"date":"2015-08-11 02:00Z","sentiment":0.63,"tweets":1874}
Reading day of timepoints for AAPL
TimeSeries timeline = new Timeline("AAPL", TimeSeries.d, proxy);
int i =0;
for (Object timepoint : timeline) {
	System.out.print(i++ + ". ");
	System.out.println(timepoint);
}
0. {"date":"2015-07-22 00:00Z","sentiment":0.62,"tweets":26528}
1. {"date":"2015-07-23 00:00Z","sentiment":0.64,"tweets":22045}
2. {"date":"2015-07-24 00:00Z","sentiment":0.65,"tweets":24992}
3. {"date":"2015-07-25 00:00Z","sentiment":0.63,"tweets":29631}
4. {"date":"2015-07-26 00:00Z","sentiment":0.42,"tweets":1436}
5. {"date":"2015-07-27 00:00Z","sentiment":0.61,"tweets":11123}
6. {"date":"2015-07-28 00:00Z","sentiment":0.65,"tweets":26182}
7. {"date":"2015-07-29 00:00Z","sentiment":0.66,"tweets":24080}
8. {"date":"2015-07-30 00:00Z","sentiment":0.66,"tweets":42599}
9. {"date":"2015-07-31 00:00Z","sentiment":0.63,"tweets":52068
Reading last 4 days of day of timepoints for AAPL
TimeSeries timeline = new Timeline("AAPL", TimeSeries.d, proxy).start("4d");
int i =0;
for (Object timepoint : timeline) {
	System.out.print(i++ + ". ");
	System.out.println(timepoint);
}
0. {"date":"2015-08-17 00:00Z","sentiment":0.65,"tweets":41620}
1. {"date":"2015-08-18 00:00Z","sentiment":0.66,"tweets":41506}
2. {"date":"2015-08-19 00:00Z","sentiment":0.66,"tweets":42823}
3. {"date":"2015-08-20 00:00Z","sentiment":0.68,"tweets":28244}
Reading minute data from a given time of day for AAPL
TimeSeries timeline = new Timeline("AAPL", TimeSeries.M).start("2015-08-19 12:34 EST").stop("2015-08-19 12:41 EST");
int i =0;
for (Object timepoint : timeline) {
	System.out.print(i++ + ". ");
	System.out.println(timepoint);
}
0. {"date":"2015-08-19 17:34Z","sentiment":0.66,"tweets":32}
1. {"date":"2015-08-19 17:35Z","sentiment":0.61,"tweets":36}
2. {"date":"2015-08-19 17:36Z","sentiment":0.74,"tweets":31}
3. {"date":"2015-08-19 17:37Z","sentiment":0.68,"tweets":41}
4. {"date":"2015-08-19 17:38Z","sentiment":0.72,"tweets":36}
5. {"date":"2015-08-19 17:39Z","sentiment":0.53,"tweets":19}
6. {"date":"2015-08-19 17:40Z","sentiment":0.72,"tweets":32}
7. {"date":"2015-08-19 17:41Z","sentiment":0.6,"tweets":35}

com.infinigongroup.api.Tweets

You can use the Tweets iterator to request tweets from any stream for a given period in time.

Tweet Data

 { 	"postedTime":"2015-08-21T00:42:08.000Z",
 	"author":"DeltaBravo33",
 	"text":"Instagram : by airbus.driver - #Qantas #QantasAirways #melbourneairport #melbourne #boeing #boeing737 #b737 #737 #m\u2026 http:\/\/t.co\/7TgGML73aA",
 	"avatar":"https:\/\/pbs.twimg.com\/profile_images\/589500634149343232\/wcMsP73m_normal.jpg"
 }

Tweet Parameters

stream

The constructor requires stream and resolution where:

  • stream - is the stream id such as AAPL or FDA
start & stop

As with Timeline you can give a date and time range by setting start and stop. stop parameter always defaults to now, while start's default value is the start of the current minute.

You can specify the dates using java.utils.Date or you can also use a String in many formats (see [Date Formats] (http://realtime.infinigongroup.com/api/docs/#data_dates)). All times are by default UTC so you must be explicit and add the timezone. You can test your date and time values using:

Time Delta

For the start parameter you can also give a time delta, specifying a period of time before the given (or default) stop date.

Period Code | Period | Example | Description --- | --- | --- | --- | --- M | minutes | "30M" | starting thirty minutes ago H | hours | "8H" | starting eight hours ago d | days |"5d" | starting five days ago w | weeks | "2w" | starting fortnight ago m | months | "3m" | starting on the same date 3 months ago y | years | "1y" | starting a year ago

Tweet Examples

Reading minute tweets for BA
TimeSeries tweets = new Tweets("FB", TimeSeries.M, proxy);
int i =0;
for (Object tweet : tweets) {
	System.out.print(i++ + ". ");
	System.out.println(tweet);
}
0. {"postedTime":"2015-08-21T00:42:00.000Z","author":"DeltaBravo33","text":"Instagram : by world_aviation99 - Good night!! Lufthansa Cargo Boeing 777F landing at Frankfurt intl. #Boeing #avpo\u2026 http:\/\/t.co\/iZWmjWSuUz","avatar":"https:\/\/pbs.twimg.com\/profile_images\/589500634149343232\/wcMsP73m_normal.jpg"}
1. {"postedTime":"2015-08-21T00:42:05.000Z","author":"kevinagipavuc","text":"RT @josephuhanokov: ? ??????? ?????? ??????????? ?? ?????????? Boeing ??? ????????? ???????? ?? ???????","avatar":"https:\/\/pbs.twimg.com\/profile_images\/523784246427537408\/OqWKikgU_normal.png"}
2. {"postedTime":"2015-08-21T00:42:08.000Z","author":"DeltaBravo33","text":"Instagram : by world_aviation99 - Good night!! Lufthansa Cargo Boeing 777F landing at Frankfurt intl. #Boeing #avpo\u2026 http:\/\/t.co\/0FZsSYizdK","avatar":"https:\/\/pbs.twimg.com\/profile_images\/589500634149343232\/wcMsP73m_normal.jpg"}
3. {"postedTime":"2015-08-21T00:42:08.000Z","author":"DeltaBravo33","text":"Instagram : by airbus.driver - #Qantas #QantasAirways #melbourneairport #melbourne #boeing #boeing737 #b737 #737 #m\u2026 http:\/\/t.co\/7TgGML73aA","avatar":"https:\/\/pbs.twimg.com\/profile_images\/589500634149343232\/wcMsP73m_normal.jpg"}

com.infinigongroup.api.Snapshot

Snapshot - Returns aggregation data for a selection of streams. Use this API to generate grids, maps, heat trees and clouds for groups of streams.

Snapshot Data

{
			"description": "Chevron Corporation",
			"sentiment": 0.558823529411764,
			"tags": [
				"Equities",
				"Energy",
				"SP500",
				"Oil & Gas",
				"DJ30"
			],
			"timestamp": {
				"$date": 1440164723564
			},
			"symbol": "CVX",
			"clout": 1292,
			"words": [
				[
					"chevron",
					18
				],
				[
					"oil",
					3
				],
				[
					"morningword",
					3
				],
				[
					"slick",
					3
				],
				[
					"xom",
					3
				],
				[
					"cvx",
					3
				],
				[
					"negra",
					2
				],
				[
					"loma",
					2
				],
				[
					"superpozo",
					2
				],
				[
					"negro",
					2
				]
			],
			"activity": 34,
			"change_5": 14,  
			"variance": -67,
			"change_3": 69,
			"change_10": 8
		}

Snapshot Fields

last_request
Gives you the timestamp in java.util.Date  of the last update.

Snapshot Parameters

since
Use this parameter to retrieve streams that have had activity since some given date. See the documentation on start. See above documentation on `start`.
streams
You can specify one or more streams, comma delimited, the default is all streams with available data. 

for (Object snapshot : new Snapshots().streams("AAPL", "GOOG")) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}
0. {"clout":9278,"sentiment":0.6392857143,"symbol":"AAPL","change_10":7,"activity":6537,"change_3":18,"variance":-94,"change_5":18,"words":[["iphone",118.0],["ipad",86.0],["apple",68.0],["gameinsight",36.0],["full",29.0],["steve",27.0],["jobs",27.0],["ipadgames",27.0],["read",19.0],["16gb",17.0]],"description":"Apple Inc","tags":["Personal Computers","Equities","SP500","Technology","PWTRADEWATCHLIST"],"timestamp":{"$date":1440282041210}}
1. {"clout":5976,"sentiment":0.7231638418,"symbol":"GOOG","change_10":13,"activity":3351,"change_3":34,"variance":-92,"change_5":20,"words":[["google",105.0],["play",22.0],["servers",19.0],["allaboutgoogle",19.0],["datacenter",19.0],["loses",18.0],["disponible",18.0],["data",18.0],["app",15.0],["consecutive",15.0]],"description":"Google Inc.","tags":["Equities","SP500","Technology","Internet Information Providers","PWTRADEWATCHLIST"],"timestamp":{"$date":1440282020217}}
tags
Instead of specifying streams you can specify category or groups of streams that we call tags. The notation is a little more developed that just a comma delimited list, so I'm going to show you some examples:
  • {DJ30}{Energy}* Streams that are in the energy sector and belong to the DJ30.
  • <DJ30> Streams that are NOT in the DJ30.
  • {Energy}<DJ30>* Streams that are in the energy sector and DO NOT belong to the DJ30.
  • {Energy}<DJ30>*{SP500}| (Streams that are in energy sector but do not belong to the DJ30) OR ( those that belong to the SP500).
Snapshots of streams that are in the energy sector and belong to the DJ30.
for (Object snapshot : new Snapshots().tags("{DJ30}{Energy}*")) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}
0. {"clout":1557,"sentiment":0.575,"symbol":"XOM","change_10":-42,"activity":155,"change_3":-47,"variance":-72,"change_5":-39,"words":[["mobil",30.0],["baru",6.0],["ini",6.0],["drive",6.0],["coba",6.0],["mau",6.0],["test",6.0],["iims",6.0],["pameran",6.0],["bisa",6.0]],"description":"Exxon Mobil Corporation","tags":["Equities","Energy","SP500","Oil & Gas","DJ30"],"timestamp":{"$date":1440282187760}}
1. {"clout":1356,"sentiment":0.5666666667,"symbol":"CVX","change_10":15,"activity":30,"change_3":-23,"variance":25,"change_5":15,"words":[["chevron",26.0],["etsy",7.0],["blue",4.0],["via",2.0],["pnnfi0xrsb",2.0],["yellow",2.0],["female",2.0],["help",2.0],["transfer",2.0],["white",2.0]],"description":"Chevron Corporation","tags":["Equities","Energy","SP500","Oil & Gas","DJ30"],"timestamp":{"$date":1440282211473}}
Snapshots of streams that are in the energy sector and DO NOT belong to the DJ30.
for (Object snapshot : new Snapshots().tags("{Energy}<DJ30>*")) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}
0. {"clout":98,"sentiment":0.0,"symbol":"SGY","change_10":3500,"activity":2,"change_3":-100,"variance":0,"change_5":-100,"words":[],"description":"Stone Energy Corp.","tags":["Equities","Energy","Independent Oil & Gas","AUTOGEN","NYSE"],"timestamp":{"$date":1440282083093}}
1. {"clout":93,"sentiment":0.25,"symbol":"E","change_10":-26,"activity":4,"change_3":-100,"variance":-13,"change_5":-100,"words":[],"description":"Eni S.P.A.","tags":["Major Integrated Oil & Gas","AUTOGEN","Equities","Energy","PWTRADEWATCHLIST","NYSE"],"timestamp":{"$date":1440282247729}}

As you can see we are using Reverse Polish Notation.

Sets of tags you want are defined using comma delimited tags in braces {}
Sets of tags you don't want are defined using comma delimited tags in braces angle brackets <>
You can union (or OR) your sets using the | pipe symbol.
You can intersect (or AND) your sets using the * star symbol.

Just remember in Reverse Polish Notation

operandA operandB operator = operandA operator operandB
operandA operandB operator1 operandC operator2 = (operandA operator1 operandB) operator2 operandC
operandA operandB operator1 operandC operandD operator2 operator3 = (operandA operator1 operandB) operator3 (operandC operator2 operandD)

For more on RPN see http://en.wikipedia.org/wiki/Reverse_Polish_notation.

words
Just as with `tags` you can filter by words in the word cloud using our RPN. The setting is character case insensitive. For instance 
'{buy}<best buy>*{fda}{approval}*|`

Returns the data for streams whose word cloud contains buy but not best buy, or any streams whose word cloud contains fda and approval.

for (Object snapshot : new Snapshots().words("{buy}<best buy>*{fda}{approval}*|")) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}
0. {"clout":2972,"sentiment":0.6,"symbol":"NDAQ","change_10":104,"activity":65,"change_3":119,"variance":18,"change_5":138,"words":[["nasdaq",65.0],["inc",35.0],["corp",6.0],["rating",4.0],["zacks",4.0],["buy",4.0],["stock",4.0],["upgraded",3.0],["sells",3.0],["lifted",2.0]],"description":"Nasdaq OMX Group Inc","tags":["Diversified Investments","SP500","Financial","Equities"],"timestamp":{"$date":1440282934625}}
1. {"clout":936,"sentiment":0.3,"symbol":"NOK","change_10":-3,"activity":96,"change_3":8,"variance":-72,"change_5":-3,"words":[["nokia",28.0],["business",9.0],["german",9.0],["car",9.0],["sale",9.0],["consortium",9.0],["maps",9.0],["confirms",9.0],["buy",4.0],["smoothblink2konga",4.0]],"description":"Nokia Corporation","tags":["Equities","Communication Equipment","Technology"],"timestamp":{"$date":1440282989083}}
2. {"clout":1412,"sentiment":0.3684210526,"symbol":"BBY","change_10":10,"activity":38,"change_3":44,"variance":0,"change_5":21,"words":[["buy",32.0],["best",32.0],["time",2.0]],"description":"Best Buy Co. Inc.","tags":["Services","Equities","SP500","PWTRADEWATCHLIST","Electronics Stores"],"timestamp":{"$date":1440283001402}}
3. {"clout":1246,"sentiment":0.4545454545,"symbol":"URBN","change_10":73,"activity":33,"change_3":74,"variance":31,"change_5":26,"words":[["outfitters",20.0],["urban",20.0],["swags",5.0],["fleeks",5.0],["sensible",5.0],["dad",5.0],["buy",5.0],["sir",5.0],["amp",5.0],["dankcharnley",5.0]],"description":"Urban Outfitters Inc","tags":["Services","Equities","SP500","PWTRADEWATCHLIST","Apparel Stores"],"timestamp":{"$date":1440283019757}}
4. {"clout":4853,"sentiment":0.5784313725,"symbol":"TGT","change_10":138,"activity":102,"change_3":427,"variance":164,"change_5":356,"words":[["target",99.0],["best",57.0],["price",55.0],["offer",12.0],["freeshipping",10.0],["get",9.0],["clothing",9.0],["amp",7.0],["mossimo",7.0],["buy",7.0]],"description":"Target Corp.","tags":["PWTRADEW","Equities","Morgan Stanley Watchlist","Variety Stores","Discount","Services","SP500"],"timestamp":{"$date":1440283019889}}
5. {"clout":35092,"sentiment":0.6952054795,"symbol":"SFS","change_10":9,"activity":878,"change_3":35,"variance":-15,"change_5":23,"words":[["smart",819.0],["full",126.0],["android",115.0],["phone",114.0],["core",93.0],["unlocked",86.0],["dual",73.0],["read",67.0],["apple",64.0],["mobile",58.0]],"description":"Smart","tags":["NYSE","Equities"],"timestamp":{"$date":1440283021015}}
6. {"clout":4033,"sentiment":0.48,"symbol":"KRFT","change_10":-16,"activity":199,"change_3":7,"variance":-50,"change_5":-1,"words":[["oreo",70.0],["nabisco",7.0],["food_you_love",4.0],["cookies",3.0],["vie",3.0],["xkingpunk",3.0],["nini_oreo",2.0],["itsfoodporn",2.0],["cheesecake",2.0],["vsak3jq71a",2.0]],"description":"Kraft Foods Group Inc","tags":["Equities","Food - Major Diversified","SP500","Consumer Goods","Morgan Stanley Watc"],"timestamp":{"$date":1440283021271}}
7. {"clout":8726,"sentiment":0.6940298507,"symbol":"AAPL","change_10":2,"activity":6438,"change_3":-1,"variance":-94,"change_5":0,"words":[["iphone",114.0],["ipad",102.0],["apple",56.0],["full",38.0],["gameinsight",32.0],["ipadgames",31.0],["read",23.0],["16gb",18.0],["ebay",17.0],["case",15.0]],"description":"Apple Inc","tags":["Personal Computers","Equities","SP500","Technology","PWTRADEWATCHLIST"],"timestamp":{"$date":1440283021303}}
8. {"clout":9035,"sentiment":0.7012448133,"symbol":"BITCOIN","change_10":-33,"activity":242,"change_3":-37,"variance":-13,"change_5":-33,"words":[["bitcoin",235.0],["free",52.0],["freebitcoin",44.0],["satoshis",41.0],["earned",31.0],["robotcoingame",31.0],["total",31.0],["robot",31.0],["left",30.0],["btc",22.0]],"description":"Bitcoin","tags":["Bitcoin Information"],"timestamp":{"$date":1440283021514}}
9. {"clout":4475,"sentiment":0.3583333333,"symbol":"NFLX","change_10":20,"activity":1164,"change_3":73,"variance":-86,"change_5":42,"words":[["netflix",116.0],["chill",31.0],["watch",17.0],["amp",11.0],["bensetters",6.0],["multiple",6.0],["let",6.0],["times",6.0],["netfixandchill",5.0],["like",4.0]],"description":"NetFlix Inc.","tags":["Equities","Music & Video Stores","Level2","PWTRADEWATCHLIST","Services","SP500"],"timestamp":{"$date":1440283021636}}
change_3
Use this parameter to filter by **percent change** in the last **3 minutes**. 

 for (Object snapshot : new Snapshots().change_3(180)) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}
0. {"clout":228,"sentiment":0.25,"symbol":"TSX:WJA","change_10":115,"activity":4,"change_3":614,"variance":1,"change_5":329,"words":[["kfll",2.0],["boeing",2.0],["aviationlovers",2.0],["avgeek",2.0],["livery",2.0],["tail",2.0],["plaid",2.0],["avporn",2.0],["westjet",2.0],["aviation",2.0]],"description":null,"tags":[],"timestamp":{"$date":1440282543164}}
1. {"clout":47,"sentiment":0.0,"symbol":"BGS","change_10":35,"activity":1,"change_3":349,"variance":167,"change_5":170,"words":[],"description":"B&G Foods Inc.","tags":["Equities","Consumer Goods","Processed & Packaged Goods"],"timestamp":{"$date":1440282543998}}
2. {"clout":47,"sentiment":0.0,"symbol":"PGR","change_10":260,"activity":1,"change_3":1100,"variance":10,"change_5":620,"words":[],"description":"Progressive Corp.","tags":["Property & Casualty Insurance","SP500","Financial","Equities"],"timestamp":{"$date":1440282604505}}
3. {"clout":91,"sentiment":0.0,"symbol":"SKYNEWS","change_10":300,"activity":1,"change_3":1234,"variance":482,"change_5":700,"words":[],"description":"Sky News","tags":["Publication"],"timestamp":{"$date":1440282615756}}
4. {"clout":44,"sentiment":0.0,"symbol":"KMB","change_10":30,"activity":1,"change_3":333,"variance":-55,"change_5":160,"words":[],"description":"Kimberly-Clark Corporation","tags":["Equities","SP500","Consumer Goods","Personal Products"],"timestamp":{"$date":1440282643536}}
5. {"clout":17,"sentiment":0.0,"symbol":"PNR","change_10":19,"activity":1,"change_3":294,"variance":238,"change_5":137,"words":[],"description":"Pentair Ltd.","tags":["Diversified Machinery","SP500","Equities","Industrial Goods"],"timestamp":{"$date":1440282729034}}
6. {"clout":46,"sentiment":0.0,"symbol":"FERGUSONTRACKER","change_10":129,"activity":20,"change_3":662,"variance":-93,"change_5":358,"words":[],"description":"Ferguson Situation","tags":["News"],"timestamp":{"$date":1440282745912}}
change_5
Use this parameter to filter by **percent change** in the last **5 minutes**. 

 for (Object snapshot : new Snapshots().change_5(180)) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}
0. {"clout":55,"sentiment":0.0,"symbol":"UFC:WANDERLEISILVA","change_10":275,"activity":2,"change_3":-100,"variance":46,"change_5":275,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440282676824}}
1. {"clout":46,"sentiment":0.0,"symbol":"FERGUSONTRACKER","change_10":129,"activity":20,"change_3":662,"variance":-93,"change_5":358,"words":[],"description":"Ferguson Situation","tags":["News"],"timestamp":{"$date":1440282745912}}
2. {"clout":27,"sentiment":0.0,"symbol":"TSX:BMO","change_10":312,"activity":1,"change_3":-100,"variance":302,"change_5":723,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440282746616}}
3. {"clout":58,"sentiment":0.0,"symbol":"GT","change_10":66,"activity":1,"change_3":-100,"variance":-70,"change_5":232,"words":[],"description":"Goodyear Tire & Rubber Co","tags":["Equities","Rubber & Plastics","SP500","Consumer Goods"],"timestamp":{"$date":1440282766247}}
4. {"clout":55,"sentiment":0.0,"symbol":"TG","change_10":66,"activity":1,"change_3":452,"variance":200,"change_5":232,"words":[],"description":"Tredegar Corp.","tags":["Equities","Rubber & Plastics","Basic Materials","AUTOGEN","NYSE"],"timestamp":{"$date":1440282793676}}
5. {"clout":125,"sentiment":0.0,"symbol":"TSX:TRI","change_10":152,"activity":3,"change_3":459,"variance":201,"change_5":235,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440282837295}}
6. {"clout":43,"sentiment":0.0,"symbol":"TSX:GMM.U","change_10":162,"activity":1,"change_3":773,"variance":-17,"change_5":424,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440282857421}}
7. {"clout":124,"sentiment":0.0,"symbol":"DDS","change_10":224,"activity":4,"change_3":170,"variance":79,"change_5":386,"words":[["people",2.0],["dillard",2.0],["mall",2.0],["lot",2.0],["department",2.0],["rocking",2.0],["blast",2.0],["saw",2.0],["store",2.0],["cool",2.0]],"description":"Dillard's Inc","tags":["Equities","NYSE","Services","Department Stores"],"timestamp":{"$date":1440282859798}}
8. {"clout":78,"sentiment":0.0,"symbol":"FXI","change_10":295,"activity":2,"change_3":558,"variance":-64,"change_5":690,"words":[],"description":"FTSE China 25 Index Fund Ishares","tags":["Amex","Equities"],"timestamp":{"$date":1440282882266}}
change_10
Use this parameter to filter by **percent change** in the last **10 minutes**. 

     for (Object snapshot : new Snapshots().change_3(300).change_5(180).change_10(300)) {
            System.out.print(i++ + ". ");
            System.out.println(snapshot);
            if (i == 20) break;
        }
0. {"clout":55,"sentiment":0.0,"symbol":"CFN","change_10":586,"activity":1,"change_3":2186,"variance":101,"change_5":1272,"words":[],"description":"CareFusion Corporation","tags":["SAC List","Health Care","Medical\/Dental Instruments","Equities"],"timestamp":{"$date":1440282914323}}
1. {"clout":51,"sentiment":0.0,"symbol":"CCE","change_10":555,"activity":1,"change_3":2082,"variance":50,"change_5":1210,"words":[],"description":"Coca-Cola Enterprises","tags":["Beverages Non-Alcoholic","SP500","Consumer Goods","Equities"],"timestamp":{"$date":1440283054172}}
2. {"clout":83,"sentiment":0.0,"symbol":"MTB","change_10":300,"activity":2,"change_3":567,"variance":-97,"change_5":300,"words":[],"description":"M&T Bank Corporation","tags":["Equities","SP500","Financial","Regional - Northeast Banks"],"timestamp":{"$date":1440283056919}}
3. {"clout":48,"sentiment":0.0,"symbol":"TSX:TOG","change_10":2780,"activity":1,"change_3":9501,"variance":0,"change_5":5660,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440283057520}}
4. {"clout":48,"sentiment":0.0,"symbol":"TSX:TMC","change_10":2780,"activity":1,"change_3":9501,"variance":0,"change_5":5660,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440283057680}}
5. {"clout":98,"sentiment":0.0,"symbol":"TPVG","change_10":4700,"activity":2,"change_3":15900,"variance":0,"change_5":9501,"words":[],"description":"Triplepoint Venture Growth Bdc","tags":["NYSE","Equities"],"timestamp":{"$date":1440283057704}}
6. {"clout":48,"sentiment":0.0,"symbol":"TSX:SGY","change_10":1958,"activity":1,"change_3":6758,"variance":0,"change_5":4015,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440283062709}}
7. {"clout":1639,"sentiment":0.6666666667,"symbol":"ZAGG","change_10":516,"activity":36,"change_3":414,"variance":1279,"change_5":550,"words":[["zagg",35.0],["ipad",33.0],["mini",33.0],["full",33.0],["apple",33.0],["gray",33.0],["retina",33.0],["space",33.0],["2nd",33.0],["book",33.0]],"description":"Zagg Inc.","tags":["Equities","NASDAQ","Specialty Retail","AUTOGEN","Consumer Non-Durables"],"timestamp":{"$date":1440283074787}}
8. {"clout":148,"sentiment":0.3333333333,"symbol":"WHLR","change_10":5301,"activity":3,"change_3":17900,"variance":0,"change_5":10700,"words":[["real",2.0],["estate",2.0],["wheeler",2.0],["whlr",2.0],["trust",2.0],["investment",2.0]],"description":"Wheeler Real Estate Investment","tags":["Equities","NASDAQ","REIT - Diversified","AUTOGEN","Real Estate"],"timestamp":{"$date":1440283099475}}
9. {"clout":148,"sentiment":0.3333333333,"symbol":"WHLRP","change_10":5301,"activity":3,"change_3":17900,"variance":0,"change_5":10700,"words":[["real",2.0],["estate",2.0],["wheeler",2.0],["whlr",2.0],["trust",2.0],["investment",2.0]],"description":"Wheeler Real Estate Investment","tags":["Nasdaq","Equities"],"timestamp":{"$date":1440283099601}}
variance
Use this parameter to filter by **variance** for the hour. So if you used a value of 30 the Snapshot iterator would return data for streams that have 30% or more tweets than the average for that stream for the given hour for that day. This is useful for consumer companies such as Mac Donald's that typically have a higher rate at lunch times and in the evenings.

for (Object snapshot : new Snapshots().variance(3000)) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}
0. {"clout":729,"sentiment":0.125,"symbol":"URE","change_10":1687,"activity":16,"change_3":2133,"variance":9500,"change_5":2803,"words":[["ure",14.0],["someone",14.0],["like",14.0],["asks",14.0],["urpri",14.0],["birthday",14.0],["coliegestudent",14.0]],"description":"Ultra Real Estate Proshares","tags":["Amex","Equities"],"timestamp":{"$date":1440283328335}}
1. {"clout":8419,"sentiment":0.6889952153,"symbol":"BEAM","change_10":1537,"activity":209,"change_3":1519,"variance":22744,"change_5":1357,"words":[["vodka",208.0],["effen",208.0],["cent",201.0],["french",200.0],["trash",199.0],["montana",199.0],["throws",109.0],["iamakademiks",88.0],["throwing",82.0],["responds",82.0]],"description":"Beam Inc.","tags":["Equities","Beverages - Wineries & Distillers","SP500","Consumer Goods"],"timestamp":{"$date":1440283429252}}
2. {"clout":754,"sentiment":0.3529411765,"symbol":"WEATHER-SOYBEANS","change_10":281,"activity":17,"change_3":199,"variance":4917,"change_5":169,"words":[["tornado",12.0],["raiders",5.0],["minnesota",4.0],["watch",4.0],["vikings",3.0],["pregame",3.0],["field",3.0],["due",3.0],["lightning",3.0],["warning",3.0]],"description":"Soybeans US Crop","tags":["Commodities"],"timestamp":{"$date":1440283437478}}
3. {"clout":754,"sentiment":0.3529411765,"symbol":"WEATHER-CORN","change_10":284,"activity":17,"change_3":201,"variance":3257,"change_5":171,"words":[["tornado",12.0],["raiders",5.0],["minnesota",4.0],["watch",4.0],["vikings",3.0],["pregame",3.0],["field",3.0],["due",3.0],["lightning",3.0],["warning",3.0]],"description":"Corn","tags":["Commodities"],"timestamp":{"$date":1440283437576}}
fields
You can specify which fields you want to retrieve using a comma delimited list. By default you get them all. The available fields are:
  • activity
  • change_3
  • change_5
  • change_10
  • description
  • sentiment
  • variance
  • words
  • tags
for (Object snapshot : new Snapshots().variance(3000).fields("variance", "sentiment")) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}
0. {"symbol":"URE","sentiment":0.125,"variance":9500}
1. {"symbol":"WEATHER-SOYBEANS","sentiment":0.2,"variance":4327}
2. {"symbol":"BEAM","sentiment":0.6723163842,"variance":19246}
order
You can use the order parameter to return the data sorted by a field, for example:
  • variance Returns the data in ascending order
  • -variance Returns the data in descending order
for (Object snapshot : new Snapshots().variance(3000).fields("variance", "sentiment").order("-sentiment")) {
	System.out.print(i++ + ". ");
	System.out.println(snapshot);
	if (i == 20) break;
}
0. {"symbol":"BEAM","sentiment":0.7006802721,"variance":15967}
1. {"symbol":"WEATHER-SOYBEANS","sentiment":0.2,"variance":4327}
2. {"symbol":"URE","sentiment":0.1,"variance":5900}

Replacing json-simple-1.1.1.jar.

  1. Override protected Object parse(BufferedReader br) to call your new JSON parser.
  2. Override protected int resultsSize() to return the size of the JSON result array.
  3. Override protected Object resultsGet(int index) to return an element of the JSON result array.
  4. Override protected Object responseGet(String key) to return an value of the JSON response object.

About

A java api for the Infinigon Social Analytical service

Resources

Readme

License

MIT license
Activity

Stars

0 stars

Watchers

2 watching

Forks

2 forks
Report repository

Releases

No releases published

Packages

No packages published

Contributors 2

  •  
  •  

Languages