AAPL Tweetbase and Document pTrees for Sentiment Analysis

AAPL For a Tweetbase, construct Docment pTrees (indexed by Term and Position)., ... Terms (Vocab) What about phrases? For 2 word phrases, use 4D cube. 5 6 .Docs Tweet3 Tweet2 Tweet1 apple always. buy AAPL April an all and are Term pTrees index by Doc, Pos. Pos 1 2… Sentiment analysis (by doc) : PSB: Positive Sentiment BitMap, 1 iff doc has positive AAPL sentiment. PSV: Positive Sentiment ValueArray, measures positive AAPL sentiment level? PSV for each term? Might term context change the sentiment? With term position information we should be able to evaluate PSV in context! PSB, PSV could be derived by hand (humans read tweets and assign a PSB or PSV). Do we need to use PS minus NS measures? (NS=Negative Sentiment) Research literature on Sentiment Analysis (word/doc sentiment assessment software ? Strategy: Each day buy the stock with the greatest Positive Sentiment Tweet Bloom? Doc Tweet3 Tweet2 Tweet1 1 1 0 1 1 1 2nd wd 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 buy, tweet1 1 0 1000 000 buy, tweet1 1 0 1000 000 AAPL, tweet1 1 0 0100 000 stronger PS than AAPL, tweet1 0 1 0000 010 determine by level1 & Docs 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 buy 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Why might the positions of words be important? e.g., “buy” and “AAPL” occur close in tweet position, - a stronger positive sentiment. If multilevel Pos pTrees, a positive sentiment bloom: again 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 all 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 always. 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 an Tweet2 Twt3 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 and 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 apple 0 0 0 0 0 0 0 0 0 April 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Tweet1 Doc pTrees (pTrees are named or index by Term and Position) Position 1 2 3 4 5 6 7 1st wd OR for Existential AAPL pTrees. Sum for AAPL tf 2wdPhraseStartPos pTree index (buy,AAPL,Tweet1) Pre-compute, save and catalog? or compute as needed by shifting AAPL TP pTree right 1-bit, then AND with the buy TP pTree. Multilevel 2wdPhraseStartPos pTrees strides= D,W,W,W-1, D=#docs, W=#wds 2-word phrase start position 1 2 3 4 5 6 1 0 0 0 0 1 Term buy Tweet1 buy 0100… AAPL 0000… AAPL AAPL… AAPL… 1 0 0 0 0 1 buy buy all always 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 . . . 0 . . . 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 . . . 0 . . . 0 . . . 0 . . . 0 . . . 0 . . . 0 . . . 0 . . . 0 . . . 1 . . . 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 1 . . . an Doc Tweet3 Tweet2 Tweet1 0 0 0 0 0 0 0 . . . Term a Etc. [Term]Pos pTrees 0 0 0 0 0 0 0 and 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 . . . apple 0 0 0 0 0 0 0 . . . 0 0 0 0 0 0 0 . . . April DocumentTermPosition pTreeSet, e.g., Tweet Sentiment Analysis (1 iff Term in Pos in Doc) OR row gives Term=a ExistentialTerm pTree. Sum gives Term=a DocFreq (df) array 7 1 2 3 4 are Positions Of Terms…

Applying the 3D pTree Text Corpus to Arijit’s first 20 AAPL tweets, APL20 (100 words). For each of the first 20 tweets. The 20 TermPosition 2D matixes: Wd Position-> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 docs 1 At one of those sensitivmoments give her 2 check out my custom trading mentorshcourse here 3 for more info on our course visit us 4 keep losing money in the market dign up 5 custom trading class with us https://$FB $AAPL 6 happy new year to all have a safe 7 take a trading course with us https://$FB 8 get stock alerts here multi-darunners https://$FB 9 day pass for our trading alerts here https:// 10 day pass for our trading alerts here https:// 11 online trading course The street mentor https://$FB 12 $SBUX three reasons Starbuckshares might stay hot 13 sign up for our trading alerts here big 14 hot list of stocks out today sign up 15 nice looking daily chart on $JAKK for a 16 been a nice run for subs from 2.7 17 sign up for our daily trading alerts and 18 send me an email for the 0.75 off 19 $IDRA could run to the 3.5 area today 20 $AAPL Apple plans investorcall ahead of a a iwatch helping the hype AAPL https://$FB $AAPL $TWTR peeps here https://$FB $AAPL $TWTR #stocks #trading for custom trading mentorshprogram with us today https:// $TWTR Peeps #investi#trading and prosperonew year #stocks #trading#investi$FB $AAPL $AAPL $TWTR Peeps #stocks #investi $AAPL $TWTR peeps $FB $AAPL $TWTR #stocks #daytrad $FB $AAPL $TWTR #stocks #daytrad $AAPL $TWTR Traders In 2015 http://t#IBDNewsvia @IBDinve$SBUX $AAPL $GOOG winners this week https://$FB $AAPL $TWTR peeps for our daily alerts here https://$FB $AAPL $TWTR swing trade here $TWTR $FB $AAPL peeps value play trade at 3.77 nice a pay day $TWTR $AAPL weekly video lessons here https://$FB $AAPL $TWTR peeps trading courses here $FB $AAPL $TWTR Traders nice breakouthere $FB $AAPL $TWTR Traders. potentiabond sale WSJ http://tvia @YahooFi $FB $AAPL $TWTR peeps $DNKN $GMCR join us http://t $BAC $FB https://t.co/vvdQfCSmj5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 2015 AAPL ahead alerts all Apple area big bond breakout call chart check course custom day email happy helping Her https://t.co/5rssX3gnPY https://t.co/DOTIjn3zTh https://t.co/gbgTQasq22 https://t.co/JCchDkM3kZ https://t.co/jGJNzj6I5i https://t.co/OSDAgXzNFf https://t.co/vvdQfCSmj5 https://t.co/XRD1mBpDdE https://t.co/ZcZhF2AWSE http://t.co/oZKXEI87ZK http://t.co/SkOpaK2vQS http://t.co/u8VxUQotTw hype info investor iwatch join keep lessons List looking losing market mentor might moments money more multi-day new nice online out pass pay peeps plans play potential Program prosperous reasons run runners safe sale send sensitive shares sign Starbucks stock street subs swing take three today trade Up value video visit week winners year $AAPL $BAC $DNKN $FB $GMCR $GOOG $IDRA $JAKK $SBUX $TWTR 0.75 2.7 3.5 3.77

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ClassWise Counts: Always use Class Order in every Classification TrainingSet E.g., X=(B1,B2,B3.B4). Let the class be B2. The ordering of a RSI table is usually a spatial pixel ordering (e.g., raster, Peano (Z), or Hilbert) Here we detail the advantage of ordering by class instead (even for spatial datasets). Rule: Always order classification TrainSet sby class. 1. Order the TrainingSet by Class (using a rough bucket sort?) . 2. Create all value pTrees for all feature attributes (e.g., B1, B3 and B4 here) 3. Create CWCT (ClassWiseCountTable) of count(PC=ci&PB=Bj) j=1,3,4, by applying CWC(PB=Bj) (ClassWiseCount operator) to each feature value bitmap (Because the TrainingSet was ordered by class, no ANDing is required any more.) Band4 11 15 11 11 11 11 11 11 15 15 11 11 15 15 11 11 Band1 3 3 7 7 3 3 7 7 2 2 10 15 2 10 15 15 Band2=Class 7 3 3 2 7 3 3 2 11 11 10 10 11 11 10 10 Band3 8 8 4 5 8 8 4 5 8 8 4 4 8 8 4 4 So the only operation is to apply to each feature value pTree, the ClassWiseCount operation. It can be applied in parallel to all feature value pTrees concurrently (on a “pTreeSet replicated” cluster) Thus, time to compute and compare (to threshold ) all Col InfoGains, is approx. time cost of one CWC So, it is paramount to fully optimize the code for the CWC operation! Substituting Pearson Correlation or Chi Square for Info Gain, CFC is all that’s needed. Important point: We never computed pairwise ANDS of class value and feature value pTrees, because we ‘ve bucket sorted the TrainingSet by class (a fast sort) before creating our value pTrees. And, again, once the CWC operator has created the table of class OneCounts for each feature column (e.g., below) the rest of the InfoGain (or Correlation or Chi Square) calculation is scalar arithmetic. Alternatively, use “Variable Length Stride” Multilevel pTrees, where leaf stride lengths are class sizes. For this Classification TrainingSet, we’d use 2-level pTrees with 5 leaf stride of lengths, 2,4,2,4,4 resp: 1. Rough [bucket] sort by class C=B1 Peano Ordered Training TableB1-vmaps B3-vmaps B4-vmaps x,y rrn CL=B2 B1 237af B3 458 B4 bf 0,0 0 7 3 01000 8 001 11 10 0,1 1 3 3 01000 8 001 15 01 1,0 2 7 3 01000 8 001 11 10 1,1 3 3 3 01000 8 001 11 10 0,2 4 3 7 00100 4 100 11 10 0,3 5 2 7 00100 5 010 11 10 1,2 6 3 7 00100 4 100 11 10 1,3 7 2 7 00100 5 010 11 10 2,0 8 11 2 10000 8 001 15 01 2,1 9 11 2 10000 8 001 15 01 3,0 a 11 2 10000 8 001 15 01 3,1 b 11 10 00010 8 001 15 01 2,2 c 10 10 00010 4 100 11 10 2,3 d 10 15 00001 4 100 11 10 X.Class.16:2,4,2,4,4.PB4=f 3,2 e 10 15 00001 4 100 11 10 3,3 f 10 15 00001 4 100 11 10 5:0,1,0,0,4 Terminology: “Class” pTree (ordered by class. List the variable stride lengths in the name. Included in the root node (since not used for anything else) total Count of full pTree followed by the 5 class counts. This should all be pre-computed and store with the pTree(then our table can be read off the root of the pTree). 1000 00 1111 00 0000 Next slides we will give more justification for the pTree rule: CW Rule: Always order any Classification Training Set by Class!(then convert to pTrees). I.e., Training Sets should always be Class Ordered!!! Click megaphones for audio CWC(PB1=7,2,6,8,c) CWC(PB1=a,2,6,8,c) etc... 3. Apply ClassWiseCount to each feature attribute Value Map pTree CWC(P,ClassOffsetList) CWC(PB4=f,2,6,8,c) etc. CWC(PB1=2,2,6,8,c) ClassStartOffsets CWC(PB1=3,2,6,8,c) • Create value pTrees for feature attributes • B1, B3, B4 X (after Class sort)B1vpTrees B3-vpTrees B4-vpTrees x,y rrn class B1 237af B3 458 B4 bf P1=f P1=2 P4=b P1=3 P1=7 P1=a P3=4 P4=f P3=5 P3=8 C= 2 bucket 0 2 0 2 0 0 1 2 3 4 5 6 7 8 9 a b c d e f 0 0 2 0 0 C= 3 bucket 0 3 2 0 2 0 2 2 0 1 C= 7 bucket 0 2 0 0 2 0 2 0 0 0 C=10 bucket 3 4 4 0 0 0 0 0 1 0 C=11 bucket 0 0 0 1 4 3 0 0 1 4 CWCT: ClassValue&FeatureVaue Count table

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