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AAPL Tweetbase and Document Trees: Analyzing Sentiment and Term Context

This article explores the use of pTrees indexed by term and position in analyzing sentiment and term context for AAPL stock. It discusses the use of positive sentiment bitmaps and value arrays and examines the importance of word positions. The article also suggests strategies for stock trading based on sentiment analysis.

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AAPL Tweetbase and Document Trees: Analyzing Sentiment and Term Context

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  1. AAPL For a Tweetbase, construct DocmentpTrees (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,P-1, D=#docs, W=#wds, P=#positions 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. 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 Position 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 Position…

  2. This movie doesn't care about cleverness, wit or any other kind of intelligent humor. Those who find ugly meanings in beautiful things are corrupt without being charming. There are slow and repetitive parts, but it has just enough spice to keep it interesting.

  3. My SA observations at this early stage: • Sentiment Analysis is done by NLP typically these days and it can certainly miss a signal of a stock rise! (i.e. the first one on this page). • SA is typically treated as [sophisticated] querying, not ad hoc data mining. • SA, as data mining, is very similar to anomaly detection, ala The Client. • SA, as a stock market recommender, must be very advanced. • The stock market seems to be driven by at least 2 35,000ft issues: • Supply and Demand • Long term company health • Since SA is a major driver, contrarianism is a good strategy (if everyone gets an SA signal to buy $AAPL then price zooms (immediately, due to microsecond trading engines) and price soars (too quickly for small guys to benefit, except, possibly, a longer term slow benefit from long term $AAPL health). • Strategy: Attack BigData (too big for horizontalists) and take a contrarian view • Strategy: look for stocks with low SA that classify as high potential based on a TooBigforVerticalProcessingofHorizontal Data (TBVPHD). We compose the TBVPHD TrainingSet over time based on a long history of 1-class classification of these “DiamondInTheRough (DITR)” stocks that rose contrary to Negative Sentiment. • How will we put together the TBVPHD TrainSet? • How will we 1-class (the DITR stocks)? • Contrarian strategies shouldn’t be based on queries but on data mining of TooBigforVerticalProcessingofHorizontalData (TBVPHD) technologies! For example, doing SA to buy stocks with high SA is a query based strategy. Even finding other stocks that are related and my therefore also rise (e.g. component supplier companies) is pretty much query based (you know what you are looking for and write a query (albeit possibly a very sophisticated query) to find it.). Whereas, effective contrarianism (in my very uneducated opinion!) should be true data mining (clustering, classification and ARM).

  4. Oblique FAUST PR = P(X dot d)<a (more answers for Arjun’s question on FAUST Hull Classifers) D≡ mRmV= oblique vector. d=D/|D| From 2013_10_12 Midpoint FAUST Classification Separate classR, classV using midpoints of means (mom) method: calc a vomV vomR d-line d v2 v1 std of these distances from origin along the d-line a View mR, mV as vectors (mR≡vector from origin to pt_mR), a = (mR+(mV-mR)/2)od = (mR+mV)/2o d(Very same formula works when D=mVmR, i.e., points to left) Training ≡ choosing "cut-hyper-plane" (CHP), which is always an (n-1)-dimensionl hyperplane (which cuts space in two). Classifying is one horizontal program (AND/OR) across pTrees to get a mask pTree for each entire class (bulk classification) Improve accuracy? e.g., by considering the dispersion within classes when placing the CHP. Use 1. the vector_of_median, vom, to represent each class, rather than mV, vomV ≡ ( median{v1|vV}, 2. project each class onto the d-line (e.g., the R-class below); then calculate the std (one horizontal formula per class; using Md's method); then use the std ratio to place CHP (No longer at the midpoint between mr [vomr] and mv [vomv] ) median{v2|vV}, ... ) dim 2 r   r vv r mR   r v v v v     r    r v mV v r v v r v dim 1

  5. FAUST Class Hulls: D=e1+e2+e3 D=e1-e3 e3 e2 PCD PCI PCD PCI D=e1+e3 e1 d-line Use the inside of a Circumscribed Training Class Hull to approximate each class, i.e., use a series of (d,a) pairs, each defining a half-space {z | doz(<|>)a}. Then the C-hull is XC&{(d,a)}Pxod(<|>)a&PC. The question remaining: How to determine the series of (d.a) pairs? 1. Choose the next d to be perpendicular to all previous (The simplest way is to use as the d series; e1, e2, ...en) 2. User the diagonals, e's, mean-to-median, mean-to-furthest, ... 3. Start with {ei}. Add a finer and finer grid of unit vectors until diameter of CIRCX is close to the diameter of C Model-based 1-class classifier for HiValue, Durable TrainingSets (e.g., C=10 yrs normal activity. Looking for anomalous activity). It may be worth the additional training time to continue to better the model in II by trimming the circumscriptorcorners further. Let CIRC1 be 1st Circumscriptor: k, define Lk={x | xk=minXk}, Hk={x | xk=maxXk}. Classify x in C iff x is in CIRC1 (minXk  xk  maxXk). (Eliminate outliers 1st? Replace minXk by lowest count change and maxXk by highest or?). Does C fill CIRC1 corners? In high dimensions, corners can be huge. a. Cap each corner with a fitted round cap (r2, r4,...)? Barrel cap??? b. diag cap  to it: e.g., D12=e1+e2 YoD12=Y1+Y2), YoD123=Y1+Y2+Y3) etc. Enclosing classes with linear bddries to sums of dim unit vectors and negatives, good for multi-class too c. Use a C-circumscribing barrel wrt each (d,a) (limits the radial reach each time, with a round cap on corners.) d. Use a C-circumscribing sphere. The ultimate Circumscriber is the convex hull, but algs for computing it are complex, even with VPHD tools created over 500 years. Can we do it with our HPVD tools (created over 10 yrs?) Circumscribe linear pieces using diagonals, {z| dozk>minz1+mnz2&dozk<mxz1+mxz2} and {z| dozk>minz1+mxz2&dozk<mxz1+mnz2}. 3D example Convex hull circumsciber, CHX. c c c cc c cc cc c c c c cc c Circumscribe linear pieces {z| dozk>min(zk)& dozk<max(zk)}, k=1,2 (i.e., d=e1,e2) Choose m1 as a pt that maximizes Distance(X, avgX) pTree Pillar k-means clustering k ireveals itself.) m1 m3 to maximize h=1..2Dis(X, mh) Choose m2 to maximize Dis(X, m1) m4 to maximize h=1..3Distance(X,mh) m4 Do until minimumh=1..kDistance(X,mh) < Threshold (or Do until mk < Threshold) This gives k. Apply pk-means. (Note we already have all Dis(X,mh)s for the first round. D=m1m2 line. Treat PCCs like parentheses - ( corresp to a PCI and ) corresponds to a PC. Each matched pair should indicate cluster somewhere in slice. Where? One could take the VoM as the best-guess centroid? Then proceed by restricting to that slice. Or 1st apply R and do PCC parenthesizing on R values to identify radial slice where cluster occurs. VoM of combo slice (linear/radial) as the centroid. Apply S to confirm. Note: A possible clustering method for identifying density clusters (as opposed to round or convex clusters) m3 m2

  6. Why I like theFAUST Linear-Spherical-RadialSerial-Parallel Classifier very much (FAUSTLSRSP) d pci2L pcd1L maxL = pcd2L minL = pci1L The parallel part lets us build a pair of Linear, Spherical and Radial hull segments for every pTree computation (the more the merrier) The serial part allows us the possibility of building a hull better than the convexhull! E.g., in a linear step, if we not only use min and max but also PCIs and PCDs, potentially we could do the following on class=@: On each PCC interval (not yet well defined n general, but in this example they are [pci1L,pcd1L] (pcd1L,pci2L) [pci2L,pcd2L] We build hull segments on each interval and OR them. In general, if non-outlier pillars m1..mi-1 have been chosen, choose mi from nop(X,{m1,...,mi-1}) (i.e., mi maximizes k=1..i-1dis2(X,mk)and is a non-outlier). (Instead of using Smi or D2NN to eliminate outliers each round, one might get better pillars by constructing Lmi-1mi:XR, eliminating outliers that show up on L, then picking the pillar to be the mean (or vector of medians) of the slice L-1[(3PCC1+PCC2)/4 , PCC2) ? ) A PCC Pillar pkmeans clusterer: Assign each (object, class) a ClassWeightReals (all CW init at 0) Classes numbered as they are revealed. As we are identifying pillar mj's, compute Lmj= Xo(mj-mj-1) and 1. For the next larger PCI in Ld(C), left-to-right. 1.1a If followed by PCD, CkAvg(Ld-1[PCI,PCD]) (or VoM). If Ck is center of a sphere-gap (or barrel gap), declare Classk and mask off. 1.1b If followed by another PCI, declare next Classk=the sphere-gapped set around Ck=Avg( Ld-1[ (3PCI1+PCI2)/4,PCI2) ). Mask it off. 2. For the next smaller PCD in Ld from the left side. 2.1a If preceded by a PCI, declare next Classk= subset of Ld-1[PCI, PCD] sphere-gapped around Ck=Avg. Mask off. 2.1b If preceded by another PCD declare next Classk=subset of same, sphere-gapped around Ck=Avg(Ld-1( [PCD2,(PCD1+PCD2)/4] ). Mask off @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ A potential advantage of the classifier: FAUST Linear-Spherical-Radial(LSR) The parallel part lets us build a pair of L,S,R hull segments for every pTree computation (the more the merrier) Serial part allows possibility of better hull than ConvexHull E.g., in a linear step, if we not only use min and max but also PCIs and PCDs, potentially we could do the following on class=@: On each PCC interval (ill-defined but here [pci1L,pcd1L] (pcd1L,pci2L) [pci2L,pcd2L] Build hull segments on each interval and OR them? Whereas the convex hull in orange (lots of false positives)

  7. Clustering with Oblique FAUST using cylindrical gaps(Building cylindrical gaps around round clusters) From 2103_10_12 But, (y-p)o(y-p) - ((y-p)od)2 =yoy-pop - (yod-pod)2 = yoy-pop - yod2 + (2pod)yod - pod2 ypdr is not a constant shift of yoy - yod2 So the answer seems to be. NO? We find yp,d gaps using yod but need p (and d) when finding ypdr gaps? ypd y d p p d ypd gap2 ypd gap1 r0 cylinder ypdgap > GWT ypd,r gap ypd,r gap ypd gap1,gap2 mask ypdrgap >GWT2 What if clusters cannot be isolated by oblique hyperplanes? We make gaps local by adding to the d-line planar gaps, cylindrical gaps around the d-line. On a dataset,Y, we use 2 real valued functionals (or SPTSs) to define cylindrical gaps, ypd=(y-p)od (for planar gaps) and ypdr=(y-p)o(y-p)-((y-p)od)2(for cylinder gaps) d=unit_vector Question? Are gaps in these SPTSs independent of p? I.e., can we simplify and always take p=origin? (y-p)o(y-p)=yoy-pop is a shift of yoy by constant, pop, and (y-p)od=yod-pod is a shift of yp≡yod by constant pod. We search (p,d,r) for large gaps in the ypdrand ypdSPTSs. We pick a gap width threshold (GWT) and search for (p,d,r) for which Gap(ypdr)>GWT2 and Gap(ypd)>GWT. So we need a pTree-based [log(n) time] Gap Finder for these cylindrical gaps Note that the ypdgap situation changes when you change r and the ypdrgap situation changes when you change d or p, so we can't just search for ypd gaps and then search for ypdr gaps or vice versa either. A pTree-based Cylindrical Gap Finder (CGF): 1. Choose a small initial radius, r0, (a*global_density/(n-1) for some a?) 2. Create an r0 cylinder mask about pd-line (Round cluster thru which pd-line runs should reveal ypd gaps even if it doesn't enclose the cluster). 3. Identify gaps in the ypdr SPTS after it is masked to the space between ypd gap1 and gap2 Are there problems here? Yes, what if the p,d-line does not pierce our cluster near its center? Next slide.

  8. Clustering with Oblique FAUST using cylindrical gaps 2 (from 2013_10_12) p d r0 cylinder yp.d gap1 yp,d gap2 ypd,,r gap ypd gap1,gap2 mask What if the pd-line doesn't pierce the cluster at its widest? yp,d gaps are still revealed but yp,d,r gaps may not be! Solution ideas? 1. Before cylinder masking (step 1) move p using gradient descent of xp,d gap width? 2. Before cylinder masking (step 1) move (p,d) using gradient descent and line search to minimize the Variance of ypd 3. Identify dense cylinders that get pieced together later? 4. Maximize each dense cylinder before finding the next? 5. We know we are in a cluster (by virtue of the fact that there are yp,d substantial gap1 and gap2) so we then move to neighboring (touching) cylinders with similar density (since they are touching there is no gap and we are confident that we are in the same cluster). 6. If we are clustering to identify outliers (anomaly detection), then the clusters we want to identify are singleton [or doubleton] sets. We can simply test each "cluster" between two gaps (and especially the end ones) for outliership (Note that we will always pierce outlier cluster at their widest). 7. For (p,d,r), r very small, do a 2Dplanar search on t=(tp,td) to maximize the variance of ypd inside the r-cylinder (this is not a gradient search but a heuristic search. The variance may not be continuously differentiable in p and d since the set of y changes as you change p or d (keeping r fixed). Also, the SPTS, ypd must be recalculated every time you change p or d). 8. For (p,d), do a 2Dplanar search on t=(tp,td) to maximize the variance of ypd over the entire space, Y. Then gradient descend to maximize the variance, then 2Dplanar search, ... CFG: Cylindrical Gap Finder: 1. Create a small radius (r0 = a*global_density/(n-1) for some a?) cylinder about the pd-line 2. Identify gaps in the yp,d,r SPTS after it's masked to the space between yp,d gap1 and gap2

  9. AAPL20 Document pTrees (1 level) (indexed by Term and Position) Pos 1 2 Term T# 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 Doc 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Apple 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 call 11 0 0 0 0 0 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  14. AAPL20Document 2lev pTrees (indexed by Term, Position; Predicate=npz; strides=5) Term T# Pos 0 1 2 3 stride http://t.co/oZKXEI87ZK 30 11: 0 0 1 0 https://t.co/OSDAgXzNFf 26 17: 1 0 0 0 https://t.co/JCchDkM3kZ 24 10: 1 0 0 0 http://t.co/u8VxUQotTw 32 13: 1 0 1 0 http://t.co/SkOpaK2vQS 32 13: 1 0 1 0 https://t.co/jGJNzj6I5i 25 7: 0 0 1 0 https://t.co/vvdQfCSmj5 27 20: 0 0 0 1 https://t.co/XRD1mBpDdE 28 6: 1 0 0 0 01000 01000 01000 00001 00001 01000 01000 01000 01000 01000 01000 01000 01000 00100 00100 00001 00001 00100 00100 00001 00100 10000 00100 10000 10000 00100 00001 10000 00001 00001 00001 00001 customer15 10: 1 0 0 0 customer15 1: 1 0 0 0 customer15 13: 1 0 0 0 AAPL 2 16: 1 0 0 0 AAPL 2 14: 1 0 0 0 Apple 6 13: 1 0 0 0 Info 34 16: 1 0 0 0 https 32 16: 1 0 0 0 2015 1 13: 1 0 0 0 Email 17 13: 1 0 0 0 https 25 :13 1 0 0 0 Her 20 13: 1 0 0 0 Alert 4 13: 1 0 0 0 https 26 16: 1 0 0 0 course14 3: 1 0 0 0 https 28 16: 1 0 0 0 course14 6: 1 0 0 0 course14 7: 1 0 0 0 https 25 16: 1 0 0 0 https 28 :13 1 0 0 0 Her 20 8: 1 0 0 0 course14 10: 0 0 0 1 helpin19 11: 1 0 0 0 Check 13 16: 1 0 0 0 helpin19 16: 1 0 0 0 ahead 3 6: 0 0 0 1 Info 34 13: 1 0 0 0 2015 1 16: 1 0 0 0 AAPL 2 2: 0 0 0 1 Area 7 13: 1 0 0 0 Area 7 7: 0 0 0 1 Info 34 3: 1 0 0 0 alert 4 12: 0 0 1 0 All 5 16: 1 0 0 0 AAPL 2 13: 1 0 0 0 https 32 16: 1 0 0 0 Hype 33 13: 1 0 0 0 alert 4 6: 0 1 1 0 Alert 4 16: 1 0 0 0 alert 4 7: 0 0 0 1 Hype 33 16: 1 0 0 0 alert 4 3: 0 1 0 0 All 5 5: 0 1 0 0 ahead 3 16: 1 0 0 0 All 5 13: 1 0 0 0 Apple 6 16: 1 0 0 0 https 32 18: 0 0 1 0 helpin19 13: 1 0 0 0 https 26 :13 1 0 0 0 Happy 18 16: 1 0 0 0 Bond 9 13: 1 0 0 0 https 30 16: 1 0 0 0 Bond 9 16: 1 0 0 0 Break 10 10: 0 0 0 1 https 30 13: 1 0 0 0 https 24 16: 1 0 0 0 https 23 13: 1 0 0 0 Break 10 13: 1 0 0 0 Call 11 16: 1 0 0 0 Chart 12 4: 0 0 1 0 Chart 12 16: 1 0 0 0 course14 13: 1 0 0 0 https 12: 0 0 1 0 Break 10 16: 1 0 0 0 Call 11 5: 0 0 0 1 https 21 13: 1 0 0 0 https 29 13: 1 0 0 0 Chart 12 13: 1 0 0 0 https 29 16: 1 0 0 0 Big 8 16: 1 0 0 0 Big 8 13: 1 0 0 0 https 29 14: 0 0 1 0 https 24 :13 1 0 0 0 https 22 13: 1 0 0 0 Area 7 16: 1 0 0 0 course14 16: 1 0 0 0 Happy 18 13: 1 0 0 0 https 27 :13 1 0 0 0 Day 16 1: 0 1 0 0 Day 16 13: 1 0 0 0 Happy 18 1: 0 1 0 0 Enail 17 16: 1 0 0 0 https 27 16: 1 0 0 0 Day 16 15: 0 0 0 1 Email 17 10: 0 0 0 1 Her 20 16: 1 0 0 0 Check 13 13: 1 0 0 0 check 13 1: 1 0 0 0 https 21 16: 1 0 0 0 https 23 16: 1 0 0 0 Big 8 8: 0 0 1 0 Day 16 16: 1 0 0 0 https 22 16: 1 0 0 0 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 00100 00100 00011 00100 00100 00100 00100 00100 00100 00100 00011 00100 00100 00100 10000 00100 00011 00011 00011 00100 00100 00100 00100 00100 00011 00100 00100 00001 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 00001 00001 00001 00100 00001 00001 00100 00001 10000 10000 00001 00001 00100 10000 00100 00100 00001 00100 00010 00001 00100 00001 01000 00001 00001 00001 01000 00001 00100 01000 00001 00001 00001 00100 00100 00001 00100 01000 00001 00001 00001 00001 00100 00001 00100 00001 00100 00100 00100 00001 00001 00100 00100 01000 00100 00100 00001 00001 00001 00001 00010 00100 01000 01000 01000 01000 01000 01000 01000 01000 00001 01000 00100 01000 01000 01000 01000 00100 01000 00001 01000 00010 00001 00001 00001 01000 00001 00001 00010 00001 00010 00001 00100 10000 00001 00100 00001 00001 01000 00001 01000 00001 00001 https://t.co/DOTIjn3zTh 22 7: 0 1 0 0 https://t.co/gbgTQasq22 23 9: 0 1 0 0 https://t.co/ZcZhF2AWSE 29 7: 0 1 0 0 https://t.co/5rssX3gnPY 21 8: 0 1 0 0 00100 00100 00100 00100 00100 00100 00100 00001 00001 00001 00011 01000 01000 00100

  15. AAPL20Doc 2lev pTrees (indexed by Term, Position; Predicate=npz; strides=5) Term T# Pos 0 1 2 3 stride Reasons 62 3: 0 0 1 0 Run 63 4: 0 0 0 1 Run 63 4: 0 0 0 1 Sign 70 7: 0 0 1 0 reasons 62 13: 1 0 0 0 reasons 62 16: 1 0 0 0 Prosperou61 10: 0 1 0 0 Run 63 13: 1 0 0 0 Run 63 16: 1 0 0 0 Program 60 13: 1 0 0 0 Runners 6416: 1 0 0 0 Runners 64 6: 0 1 0 0 Market 43 6: 1 0 0 0 Safe 6513: 1 0 0 0 Safe 65 6: 0 1 0 0 Runners 6413: 1 0 0 0 prosperou61 13: 1 0 0 0 Shares 69 5: 0 0 1 0 Shares 6916: 1 0 0 0 Sale 66 13: 1 0 0 0 Sign 7013: 1 0 0 0 Take 76 13: 1 0 0 0 Take 76 10: 0 1 0 0 Take 76 16: 1 0 0 0 Swing 75 9: 0 0 1 0 Swing 75 16: 1 0 0 0 Swing 75 13: 1 0 0 0 Subs 74 6: 0 0 0 1 Subs 74 16: 1 0 0 0 subs 74 13: 1 0 0 0 Shares 6913: 1 0 0 0 Sale 66 16: 1 0 0 0 sensitive6813: 1 0 0 0 Send 67 16: 1 0 0 0 lessons 39 11: 0 0 0 1 Losing 42 16: 1 0 0 0 Losing 42 13: 1 0 0 0 List 40 16: 1 0 0 0 keep 38 16: 1 0 0 0 Looking 41 2: 0 0 1 0 Join 37 16: 1 0 0 0 keep 38 1: 1 0 0 0 List 40 13: 1 0 0 0 Join 37 13: 1 0 0 0 Looking 41 16: 1 0 0 0 Market 43 13: 1 0 0 0 lessons 39 13: 1 0 0 0 Looking 41 13: 1 0 0 0 Investor35 16: 1 0 0 0 Losing 42 2: 1 0 0 0 lessons 39 16: 1 0 0 0 List 40 2: 0 0 1 0 Iwatch 36 10: 1 0 0 0 keep 38 13: 1 0 0 0 Investor35 13: 1 0 0 0 Market 43 16: 1 0 0 0 Safe 6516: 1 0 0 0 sensitive68 5: 1 0 0 0 Sensitive6816: 1 0 0 0 Street 73 5: 0 0 1 0 Send 67 1: 0 0 0 1 Send 67 13: 1 0 0 0 Street 7316: 1 0 0 0 Sale 66 3: 0 0 0 1 Stock 72 2: 0 1 0 0 Might 45 13: 1 0 0 0 Mentor 44 6: 0 0 1 0 Mentor 44 16: 1 0 0 0 Mentor 44 13: 1 0 0 0 potential5913: 1 0 0 0 online 5216: 1 0 0 0 out 5313: 1 0 0 0 pass 5416: 1 0 0 0 Play 58 16: 1 0 0 0 Play 58 13: 1 0 0 0 Plans 57 3: 0 0 0 1 Plans 57 16: 1 0 0 0 Plans 57 13: 1 0 0 0 Pay 5514: 0 0 0 1 Pay 5513: 1 0 0 0 out 5316: 1 0 0 0 pass 54 2: 0 1 0 0 Pay 5516: 1 0 0 0 pass 5413: 1 0 0 0 Nice 5112: 0 0 0 1 Nice 51 9: 0 0 0 1 Nice 51 3: 0 0 0 1 Nice 51 1: 0 0 1 0 Nice 5116: 1 0 0 0 Nice 5113: 1 0 0 0 out 53 5: 0 0 1 0 out 53 2: 1 0 0 0 Might 45 16: 1 0 0 0 Might 45 6: 0 0 1 0 Street 7313: 1 0 0 0 Moments 46 13: 1 0 0 0 Stock 72 4: 0 0 1 0 Stock 7216: 1 0 0 0 Stock 7213: 1 0 0 0 Starbucks71 4: 0 0 1 0 starbucks7116: 1 0 0 0 starbucks7113: 1 0 0 0 Sign 70 1: 0 0 1 0 Sign 7016: 1 0 0 0 Program 60 16: 1 0 0 0 prosperou61 16: 1 0 0 0 Potential5916: 1 0 0 0 online 52 1: 0 0 1 0 online 5213: 1 0 0 0 New 5011: 0 1 0 0 New 5016: 1 0 0 0 New 5013: 1 0 0 0 Multi-day49 5: 1 0 0 0 Multi-day4916: 1 0 0 0 Multi-day4913: 1 0 0 0 More 48 2: 1 0 0 0 More 48 16: 1 0 0 0 More 48 13: 1 0 0 0 Money 47 3: 1 0 0 0 Money 47 16: 1 0 0 0 Money 47 13: 1 0 0 0 Moments 46 6: 1 0 0 0 Moments 46 16: 1 0 0 0 New 50 2: 0 1 0 0 01000 00001 01000 10000 00010 00001 00001 00100 00100 00100 10000 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 10000 00100 00100 10000 00100 00100 00100 00100 00100 00100 00100 00100 01000 00100 00100 00100 00100 00100 10000 00100 00100 00100 00100 00100 00100 00100 00100 00100 10000 00100 10000 00100 00100 00100 10000 00100 00100 00100 00100 00100 10000 00100 00100 00100 10000 00100 10000 10000 00100 00100 10000 10000 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 00100 10000 00100 10000 00100 10000 00100 10000 00100 10000 10000 00100 00100 00100 10000 10000 00011 10000 00100 00100 00100 00100 00100 10000 00100 00100 00100 10000 10000 00100 10000 00100 00100 00100 00100 00100 00100 00100 10000 10000 00100 00100 00100 10000 00100 00100 00100 00010 00100 00100 00100 00100 00100 00010 00100 00100 00001 00001 00010 10000 00100 01000 00100 00010 00100 00100 10000 00001 00100 00100 00001 00100 00100 00100 00100 00001 00011 00010 00100 00001 00001 00100 00100 00100 01000 00100 00001 00001 00100 00100 00010 00100 00100 00010 00001 00100 00100 00100 00001 00010 00100 00100 00001 00100 10000 00001 00100 00100 10000 00001 00100 00001 00100 00100 10000 00001 00100 10000 00001 00100 00011 00001 00100 00100 00100 00011 00001 00010 00100 00100 00001 00100 00001 00100 00010 00001 00100 00001 00100 00100 10000 00100 00001 00001 10000 00100 00100 00011 00100 00001 00100 00001 01000 00100 00100 01000 10000 00001 00010 00100 01000 01000 01000 01000 10000 01000 01000 00100 00100 01000 01000 01000 01000 01000 00001 01000 01000 01000 01000 00100 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 00001 01000 01000 01000 01000 00100 00100 01000 01000 01000 01000 01000 01000 01000 01000 01000 00001 00010 01000 01000 01000 01000 01000 01000 01000 00100 00010 00100 00100 00100 01000 01000 00100 01000 01000 10000 00100 01000 01000 00100 01000 01000 01000 01000 01000 00100 01000 01000 00100 00001 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 00100 01000 01000 01000 01000 01000 01000 01000 00100 01000 01000 01000 01000 01000 01000 00100 01000 01000 01000 00010 00100 00100 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 00001 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 10000 01000 01000 01000 01000 00001 01000 01000 01000 00100 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 10000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 10000 01000 01000 01000 01000 01000 00010 01000 10000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 01000 00010 01000 10000 01000 01000 00001 00001 00001 00001 00001 00001

  16. AAPL20Doc 2lev pTrees (indexed by Term, Position; Predicate=npz; strides=5) Term T# Pos 0 1 2 3 stride Doc 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Trade 7911: 1 0 0 0 $BAC 8818: 0 0 0 1 $TWTR 9617: 0 0 1 0 $SBUX 9517: 0 0 1 0 $AAPL 87 1: 0 0 0 1 3.77 10011: 0 0 0 1 Today 78 8: 0 0 0 1 Trade 79 2: 0 0 0 1 $AAPL 8715: 0 0 0 1 3.5 99 6: 0 0 0 1 Year 86 3: 0 1 0 0 $AAPL 8710: 0 1 0 0 $SBUX 95 1: 0 0 1 0 2.7 98 8: 0 0 1 0 $JAKK 94 6: 0 0 0 1 $GOOG 9217: 0 0 0 1 $TWTR 9618: 0 1 0 0 $FB 9016: 1 1 0 0 $FB 90 8: 0 1 1 0 $TWTR 9615: 0 0 1 0 $TWTR 9614: 0 0 0 1 $FB 90 9: 0 1 0 0 $DNKN 8918: 0 0 0 1 $TWTR 9611: 0 1 0 0 $TWTR 9610: 0 1 1 0 $FB 9015: 0 0 1 0 Trade 79 3: 0 1 0 0 TRADE 79 9: 0 0 0 1 $TWTR 96 9: 1 0 0 0 $TWTR 9612: 1 0 1 0 Today 78 6: 0 0 1 0 TRADE 79 6: 0 0 0 1 0.75 9716: 1 0 0 0 Year 8613: 1 0 0 0 $FB 9010: 1 0 0 0 $AAPL 8717: 0 1 0 1 $FB 90 7: 1 0 0 0 $FB 9012: 0 0 0 1 $FB 9011: 1 0 0 0 winners 8513: 1 0 0 0 $TWTR 9613: 1 0 0 0 Term 8016: 1 0 0 0 Trade 7913: 1 0 0 0 $GOOG 9216: 1 0 0 0 $TWTR 9616: 1 0 0 0 Today 7816: 1 0 0 0 visit 83 7: 1 0 0 0 Term 80 2: 0 0 1 0 3.77 10013: 1 0 0 0 Trade 7910: 0 0 1 0 3.77 10016: 1 0 0 0 $GOOG 9213: 1 0 0 0 Today 7813: 1 0 0 0 Trade 7916: 1 0 0 0 3.5 9913: 1 0 0 0 $IDRA 93 1: 0 0 0 1 2.7 9816: 1 0 0 0 0.75 9713: 1 0 0 0 Term 80 8: 1 0 1 0 2.7 9813: 1 0 0 0 TRADE 79 5: 1 1 1 0 $AAPL 8712: 1 0 0 0 3.5 9916: 1 0 0 0 Winners 85 6: 0 0 1 0 Term 8013: 1 0 0 0 $JAKK 9416: 1 0 0 0 $JAKK 9413: 1 0 0 0 $IDRA 9316: 1 0 0 0 $IDRA 9313: 1 0 0 0 value 8113: 1 0 0 0 $AAPL 8713: 1 0 0 0 $GMCR 9116: 1 0 0 0 video 8213: 1 0 0 0 $GMCR 9113: 1 0 0 0 $FB 9018: 1 0 0 0 Video 8210: 0 0 0 1 $AAPL 8716: 1 0 1 0 $FB 9014: 0 0 0 1 visit 8313: 1 0 0 0 $SBUX 9513: 1 0 0 0 Year 8616: 1 0 0 0 $FB 9019: 0 0 0 1 Winners 8516: 1 0 0 0 $SBUX 9516: 1 0 0 0 Value 8116: 1 0 0 0 $AAPL 8714: 0 0 1 0 visit 8316: 1 0 0 0 $DNKN 8916: 1 0 0 0 Week 84 9: 0 0 0 1 $DNKN 8913: 1 0 0 0 $FB 9013: 1 0 1 0 Week 8413: 1 0 0 0 $BAC 8816: 1 0 0 0 $BAC 8813: 1 0 0 0 Week 8416: 1 0 0 0 Week 8411: 0 0 1 0 video 8216: 1 0 0 0 $AAPL 87 9: 0 1 1 0 $AAPL 8719: 1 0 0 0 $AAPL 87 8: 1 0 0 0 00011 10000 00100 10000 00100 10000 00100 00100 00011 10000 01100 00100 00011 10000 10000 10000 10000 00100 00011 00100 00100 01100 10000 00011 00100 00011 10000 10000 00100 10000 10000 00100 10000 00100 10000 00011 00100 00100 00100 01000 00100 00100 10000 00011 00011 10000 00011 00100 00011 10000 10000 10000 00011 00100 00100 10000 00100 00100 00100 10000 00100 00100 00100 10000 10000 00100 00100 00100 00100 10000 10000 00100 00100 01100 10000 10000 00100 00100 10000 00100 00100 10000 00100 00100 00100 10000 00100 00100 00100 00100 00100 10000 10000 00100 00100 10000 10000 10000 10000 00100 00010 01000 00010 00010 00001 00001 00010 10000 00010 00101 00001 01000 00100 00001 00100 01000 00010 01000 00010 00100 10000 00001 00001 01000 01000 00001 00100 00001 00001 01000 00001 00001 01000 00100 00001 00001 00100 00100 00001 00100 00010 00100 00010 00100 00100 00100 00100 00001 00100 00010 01000 00100 00010 00001 00001 00001 00001 00100 00001 00100 00100 00100 00100 00100 00001 00100 00001 00100 00010 01000 01000 00001 00010 00010 00100 00100 00100 00001 00001 00010 00100 00100 00001 00010 00010 00010 10000 00100 00001 00001 00100 00001 00100 00010 00100 00100 00100 00001 00001 00010 00100 00100 00100 01000 00100 00100 00100 01000 00100 00100 00010 00101 01000 00100 00100 00010 00010 00100 00100 00010 01000 01000 01000 00100 01000 10000 01000 00100 00100 00100 00010 00100 00010 01000 01000 00100 01000 00010 01000 01000 00010 00100 01000 01000 00100 00100 00100 01000 01000 01000 00100 00010 00100 01000 00001 00100 01000 00100 01000 01000 00001 00010 01000 00100 01000 00010 00100 00100 01000 01000 00100 00100 00100 00010 10000 01000 00010 01000 00100 00100 00100 00100 01000 00010 01000 01000 00100 10000 00100 01000 00101 01000 00100 01000 00100 00010 00010 00010 01000 00001 01000 01000 01000 10000 00010 01000 01000 01000 01000 01000 01000 01000 01000 01000 00110 10000 01000 01000 01000 01000 10000 01000 10000 01000 00010 01000 00001 10100 01000 01000 10000 10000 00010 10000 01000 01000 01000 01000 10000 10000 01000 00010 00010 01000 00110 01000 01000 01000 00010 00001 10000 01000 01000 01000 01000 01000 01000 01000 01000 01000 10000 10000 01000 00010 01000 01000 01000 00010 10000 11000 01000 01000 01000 01000 00010 00110 01000 01000 01000 01000 01000 01000 10000 01000 01000 10000 01000 10000 01000 01000 01000 01000 01000 10000 01000 01000 00010 01000 01000 01000 Pos 1 2 Term T# 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0

  17. AAPL20 Position 2-Level pTrees Index by (Term,Document) Predicate= npz; Stride=5 DOC2 https://t.co/gbgTQasq22 check course custom out peeps trade $AAPL $FB $TWTR DOC4 https://t.co/OSDAgXzNFf custom keep losing market money program sign today trade up $AAPL$FB $TWTR DOC3 https://t.co/JCchDkM3kZ course info more visit $AAPL $FB $TWTR DOC1 AAPL helping her hype iwatch moments sensitive T# Lev1 Lev0 2 0 0 1 0 0 0 0 1 0 19 0 0 1 0 1 0 0 0 0 20 0 1 0 0 0 0 1 0 0 36 0 1 0 0 0 0 0 0 1 46 0 1 0 0 1 0 0 0 1 33 0 0 1 0 0 0 1 0 0 68 1 0 0 0 0 0 0 0 0 13 1 0 0 0 1 0 0 0 0 87 0 0 1 0 1 0 0 0 0 90 0 1 0 0 0 0 0 0 1 14 0 1 0 0 0 1 0 0 0 15 1 0 0 0 0 0 0 1 0 56 0 0 1 0 0 0 1 0 0 79 1 0 0 0 0 0 0 0 1 14 0 1 0 0 1 0 0 0 0 24 0 1 0 0 0 0 0 0 1 96 0 0 1 0 0 1 0 0 0 15 1 0 0 0 0 0 0 1 0 23 0 1 0 0 0 0 0 1 0 83 0 1 0 0 0 1 0 0 0 34 1 0 0 0 0 0 1 0 0 48 1 0 0 0 0 1 0 0 0 87 0 0 1 0 0 1 0 0 0 90 0 0 1 0 1 0 0 0 0 96 0 0 1 0 0 0 1 0 0 15 0 1 0 0 0 0 0 0 1 26 0 0 0 1 0 1 0 0 0 79 0 0 1 0 1 0 0 0 0 43 0 1 0 0 1 0 0 0 0 78 0 0 0 1 1 0 0 0 0 80 0 1 0 0 0 0 1 0 0 87 0 0 0 1 0 0 0 1 0 90 0 0 0 1 0 0 1 0 0 38 1 0 0 0 1 0 0 0 0 42 1 0 0 0 0 1 0 0 0 47 1 0 0 0 0 0 1 0 0 60 0 0 1 0 0 0 1 0 0 70 0 1 0 0 0 1 0 0 0 96 0 0 0 1 0 0 0 0 DOC6 all happy new peeps prosperous safe year $AAPL $FB $TWTR DOC7 https://t.co/DOTIjn3zTh Course peeps take trade $AAPL $FB $TWTR DOC5 https://t.co/XRD1mBpDdE course custom peeps trade $AAPL $FB $TWTR 90 0 0 0 1 1 0 0 0 0 96 0 0 0 1 0 0 1 0 0 65 0 1 0 0 0 0 1 0 0 14 1 0 0 0 0 0 1 0 0 15 1 0 0 0 1 0 0 0 0 61 0 1 0 0 0 0 0 0 1 86 1 0 1 0 0 0 1 0 0 87 0 0 0 1 0 1 0 0 0 87 0 1 0 0 0 0 0 1 0 87 0 1 0 0 0 0 1 0 0 79 1 0 0 0 0 0 1 0 0 90 0 1 0 0 0 0 1 0 0 96 0 1 0 0 0 0 0 0 1 56 0 0 0 1 0 0 0 1 0 14 1 0 0 0 0 0 0 1 0 22 0 1 0 0 0 1 0 0 0 28 0 1 0 0 1 0 0 0 0 79 1 0 0 0 0 1 0 0 0 90 0 1 0 0 0 1 0 0 0 96 0 1 0 0 0 0 0 1 0 5 1 0 0 0 0 0 0 0 1 18 1 0 0 0 1 0 0 0 0 50 1 0 1 0 0 1 0 0 0 76 1 0 0 0 1 0 0 0 0 56 0 1 0 0 0 0 0 0 1 56 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 DOC8 https://t.co/ZcZhF2AWSE alerts multi-day peeps runners stock $AAPL $FB $TWTR 96 0 1 0 0 0 0 0 0 0 72 1 0 0 0 0 1 0 0 0 64 0 1 0 0 1 0 0 0 0 87 0 1 0 0 0 0 0 0 0 90 0 1 0 0 0 0 0 0 0 56 0 0 1 0 1 0 0 0 0 4 1 0 0 0 0 0 1 0 0 49 1 0 0 0 0 0 0 0 1 29 0 1 0 0 0 1 0 0 0 DOC10 https://t.co/5rssX3gnPY alerts day pass trade $AAPL $FB $TWTR DOC9 https://t.co/5rssX3gnPY alerts day pass trade $AAPL $FB $TWTR 16 1 0 0 0 1 0 0 0 0 21 0 1 0 0 0 0 1 0 0 79 1 0 0 0 0 0 0 0 1 87 0 1 0 0 0 0 0 0 1 4 0 1 0 0 1 0 0 0 0 96 0 0 1 0 1 0 0 0 0 54 1 0 0 0 0 1 0 0 0 90 0 1 0 0 0 0 0 1 0 16 1 0 0 0 1 0 0 0 0 21 0 1 0 0 0 0 1 0 0 79 1 0 0 0 0 0 0 0 1 87 0 1 0 0 0 0 0 0 1 4 0 1 0 0 1 0 0 0 0 96 0 0 1 0 1 0 0 0 0 54 1 0 0 0 0 1 0 0 0 90 0 1 0 0 0 0 0 1 0 DOC12 http://t.co/oZKXEI87ZK 2015 might reasons shares Starbucks three $AAPL $DNKN $GMCR $GOOG DOC13 https://t.co/ZcZhF2AWSE alerts big peeps sign trade up week winners $AAPL $BAC $FB $TWTR 89 0 0 0 1 0 0 1 0 0 92 0 0 0 1 0 1 0 0 0 91 0 0 0 1 0 0 0 1 0 30 0 0 1 0 1 0 0 0 0 45 0 1 0 0 1 0 0 0 0 69 1 0 0 0 0 0 0 0 1 71 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 1 77 1 0 0 0 0 1 0 0 0 DOC11 https://t.co/jGJNzj6I5i course mentor online street trade $AAPL $FB $TWTR 62 1 0 0 0 0 0 1 0 0 87 0 0 0 1 1 0 0 0 0 87 0 0 1 0 0 0 0 1 0 88 0 0 0 0 0 0 0 0 0 DOC14 https://t.co/ZcZhF2AWSE Alerts join list out sign stock today up $AAPL $FB $TWTR 70 1 0 0 0 1 0 0 0 0 79 1 0 0 0 0 0 0 0 1 96 0 0 1 0 0 0 0 0 1 85 0 1 0 0 0 0 0 1 0 90 0 0 1 0 0 0 1 0 0 84 0 0 1 0 1 0 0 0 0 8 0 1 0 0 0 0 1 0 0 29 0 0 1 0 0 1 0 0 0 4 0 1 0 0 1 0 0 0 0 56 0 0 0 1 1 0 0 0 0 80 1 0 0 0 0 1 0 0 0 25 0 1 0 0 0 1 0 0 0 44 0 1 0 0 1 0 0 0 0 73 1 0 0 0 0 0 0 0 79 1 0 0 0 0 1 0 0 0 14 1 0 0 0 0 0 1 0 0 96 0 1 0 0 0 0 0 0 1 52 1 0 0 0 1 0 0 0 0 87 0 1 0 0 0 0 0 1 0 90 0 1 0 0 0 0 1 0 0 80 0 1 0 0 0 0 1 0 0 87 0 0 0 1 1 0 0 0 0 29 0 0 1 0 0 0 0 1 0 37 0 0 0 1 0 0 1 0 0 4 0 0 1 0 0 1 0 0 0 96 0 0 0 1 0 1 0 0 0 90 0 0 1 0 0 0 0 0 1 78 0 1 0 0 1 0 0 0 0 40 1 0 0 0 0 1 0 0 0 72 1 0 0 0 0 0 0 1 0 53 1 0 0 0 0 0 0 0 1 70 0 1 0 0 0 1 0 0 0 DOC16 https://t.co/vvdQfCSmj5 day pay run subs trade $AAPL $BAC $FB $TWTR 2.7 3.77 DOC18 course email send trade $AAPL $FB $TWTR 0.75 DOC17 https://t.co/ZcZhF2AWSE Alerts lessons peeps sign trade up video week $AAPL $FB $TWTR 16 0 0 1 0 0 0 0 0 0 27 0 0 0 1 0 0 0 0 1 63 1 0 0 0 0 0 0 1 0 74 0 1 0 0 1 0 0 0 0 79 0 1 0 0 0 0 0 1 0 87 0 0 0 1 0 1 0 0 0 88 0 0 0 1 0 0 1 0 0 90 0 0 0 1 0 0 0 1 0 55 0 0 1 0 0 0 0 1 0 96 0 0 0 1 1 0 0 0 0 98 0 1 0 0 0 0 1 0 0 100 0 0 1 0 1 0 0 0 0 DOC15 http://t.co/u8VxUQotTw chart looking nice peeps play swing trade value $AAPL $FB $JAKK $TWTR 14 0 1 0 0 0 0 0 0 1 17 1 0 0 0 0 0 0 1 0 79 0 1 0 0 0 0 0 1 0 87 0 0 1 0 0 0 1 0 0 90 0 0 1 0 0 1 0 0 0 96 0 0 1 0 0 0 0 1 0 97 0 1 0 0 0 1 0 0 0 67 1 0 0 0 1 0 0 0 0 56 0 0 0 1 0 1 0 0 0 70 1 0 0 0 1 0 0 0 0 79 0 1 0 0 1 0 0 0 0 80 1 0 0 0 0 1 0 0 0 82 0 1 0 0 0 0 0 0 1 96 0 0 0 1 1 0 0 0 0 39 0 0 1 0 1 0 0 0 0 84 0 1 0 0 0 0 0 1 0 87 0 0 1 0 0 0 0 0 1 90 0 0 1 0 0 0 0 1 0 4 0 1 0 0 0 1 0 0 0 29 0 0 1 0 0 0 1 0 0 87 0 0 1 0 0 0 0 1 0 90 0 0 1 0 0 0 1 0 0 32 0 0 0 1 0 0 1 0 0 41 1 0 0 0 0 1 0 0 0 12 1 0 0 0 0 0 0 1 0 96 0 0 1 0 0 1 0 0 0 81 0 0 0 1 1 0 0 0 0 94 0 1 0 0 1 0 0 0 0 79 0 1 0 0 0 0 0 0 1 56 0 0 1 0 0 0 0 0 1 58 0 0 0 1 0 1 0 0 0 51 1 0 0 0 1 0 0 0 0 75 0 1 0 0 0 0 0 1 0 • Kphrase(in sequence) and KsetpTrees: Ksets are created by OR-ing the kTermpTrees. Note 2PhraseStartPos pTrees would involve #docs*#Words*#Words*#Pos cells so it could be too massive even for pTree technology! Terms=1Terms. Use downward closure (of ARM) to facilitate fast pTree creation for kPhrases from . • We’ll select “content” kPhrases and create Position pTrees for them (with start positions). Since tweets are short and the vocab is long, it might be best to do this by just scanning the tweets by hand? I selected the following content 2phrases from AAPL20: • custom trade; losing money; stock alert; multi-day runners; trade alerts; online trade; street mentor; daily chart; nice run; investor call; big winners; trade courses; nice breakout; customer trade; bond sale; trade mentorship; new year; daily alerts; value play. • Each word in a content kphrase must be a content Term and be selected as a content kphrase (ALA, ARM downward closure). At the kth step we construct candidate content kphrases from pairs of content (k-1) pairs that coincide on (k-2) terms. Then delete all such kphrases that are non-content and/or have zero count. DOC20 http://t.co/SkOpaK2vQS AAPL ahead bond call investor plans potential sale $AAPL 31 0 0 1 0 0 0 1 0 0 35 1 0 0 0 0 0 0 1 0 57 1 0 0 0 0 0 1 0 0 87 1 0 0 0 1 0 0 0 0 9 0 1 0 0 0 0 0 0 1 59 0 1 0 0 0 0 0 1 0 66 0 0 1 0 1 0 0 0 0 2 1 0 0 0 0 1 0 0 0 3 0 1 0 0 1 0 0 0 0 11 1 0 0 0 0 0 0 0 1 DOC19 area breakout nice run today $AAPL $FB $IDRA $TWTR 3.5 78 0 1 0 0 0 0 1 0 0 87 0 0 1 0 0 0 1 0 0 90 0 0 1 0 0 1 0 0 0 93 1 0 0 0 1 0 0 0 0 51 0 1 0 0 0 0 0 1 0 96 0 0 1 0 0 0 0 1 0 99 0 1 0 0 1 0 0 0 0 7 0 1 0 0 0 1 0 0 0 10 0 1 0 0 0 0 0 0 1 63 1 0 0 0 0 0 1 0 0 Wds 87,88 (@APPL #BAC) in doc16, pos=17,18

  18. Kphrases(in sequence): KSets (OR ofTermpTrees) used like keyword lists (create on the fly?). For kphrases, use downward closure to facilitate fast pTree creation for kPhrases AAPL20 Select “content” kPhrases (StartPosition pTree = 1st Term SP) I selected these from AAPL20 by hand: sensitive moments, iwatch helping, helping hype, hype AAPL, custom trade; losing money; stock alert; multi-day runners; trade alerts; online trade; street mentor; daily chart; nice run; investor call; big winners; trade courses; nice breakout; customer trade; bond sale; trade mentorship; new year; daily alerts; value play. 1. At one of those sensitive moments 2. Check out my custom trading mentorship 3. For more info on our courses 4. Keep losing money in the market? 5. Custom trading classes with us. https://t.co 6. Happy new year to all! Have 7. Take a trading course with us! 8. Get stock alerts here multi-day runners! 9. Day pass for our trading alerts 10. Day pass for our trading alerts 11. Online Trading Course's The Street Mentor 12. $SBUX Three Reasons Starbucks Shares Might 13. Sign up for our trading alerts 14. Hot list of stocks out today 15. Nice looking daily chart on $JAKK 16. Been a nice run for sub's 17. Sign up for our daily trading 18. Send me an email for the 19. $IDRA could run to the $3.50 20. $AAPL Apple plans investor call ahead give her a iwatch helping the courses here: https://t $AAPL $TWTR visit us here: https $FB $AAPL Sign up for a custom trading $FB $AAPL $TWTR Peeps #investing #trading a safe and prosperous new year! https://t.co $FB $AAPL $TWTR Peeps #stocks https://t.co $FB $AAPL $TWTR peeps here: https://t.co$FB $AAPL $TWTR #stocks here: https://t.co$FB $AAPL $TWTR #stocks https://t.co $FB $AAPL $TWTR Traders. Stay Hot In 2015 http: #IBDNews here Big winners this week. https: Sign up for our daily alerts for a swing trade here. $TWTR from $2.70 trade' at $3.77 AH_ alerts and weekly video lessons here! 75% off trading courses here $FB area today. Nice breakout here. $FB of a potential bond sale: WSJ $AAPL $TWTR peeps. $GMCR us! u8V $FB https hype AAPL peeps. $TWTR #stocks #trading mentorship program with today https $FB #stocks #trading #investig $FB $AAPL $TWTR #investing #daytrading #daytrading via @IBDinvestor$SBUX $AAPL $GOOG $DNKN $FB $AAPL $TWTR Peeps here! https://t.co$FB $AAPL $TWTR Join $FB $AAPL Peeps value play. http: Nice pay day $TWTR $AAPL $BAC https $AAPL $TWTR Peeps $AAPL $TWTR Traders $AAPL $TWTR Traders. http @YahooFinance19. DOC1 Sensitive moment her iwatch helping hype AAPL sensitive-moments iwatch-helping hype-AAPL T# Lev1 Lev0 33 0 0 1 0 0 0 1 0 0 19 0 0 1 0 1 0 0 0 0 46 0 1 0 0 1 0 0 0 0 20 0 1 0 0 0 0 1 0 0 36 0 1 0 0 0 0 0 0 1 2 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 68 1 0 0 0 0 0 0 0 1 &46= 0 1 0 0 1 0 0 0 0 68 0 1 0 0 1 0 0 0 0 So we might just indicate in the header if a Term launches a Content2Phrase, Content3Phrase, … as follows: Phrases: 2 2 2 DOC1 Sensitive moment her iwatch helping hype AAPL T# Lev1 Lev0 33 0 0 1 0 0 0 1 0 0 19 0 0 1 0 1 0 0 0 0 46 0 1 0 0 1 0 0 0 0 20 0 1 0 0 0 0 1 0 0 36 0 1 0 0 0 0 0 0 1 2 0 0 1 0 0 0 0 1 0 68 1 0 0 0 0 0 0 0 1 Observations: On the previous page I sorted using a software that put the terms in a strange order (which is not a problem since we fully shuffle pTree placements anyway for our pTreeSet Security Mechanism anyway). However on this page I put them in position order so we could indicate phrases easily. If we shuffle, the phrase array can be as shown here and matched up to the Term list after retrieval. Also note that I discovered many errors on the previous page (sorry – too lazy to go through and correct them all). Phrases: 2 3 2 DOC13 sign up trade alerts course big winners week https://t.co/ZcZhF2AWSE $FB $AAPL $TWTR peeps T# Lev1 Lev0 79 1 0 0 0 0 0 0 0 1 4 0 1 0 0 1 0 0 0 0 84 0 0 1 0 1 0 0 0 0 90 0 0 1 0 0 0 1 0 0 87 0 0 1 0 0 0 0 1 0 56 0 0 0 1 1 0 0 0 0 70 1 0 0 0 1 0 0 0 0 80 1 0 0 0 0 1 0 0 0 85 0 1 0 0 0 0 0 1 0 29 0 0 1 0 0 1 0 0 0 96 0 0 1 0 0 0 0 0 1 14 0 1 0 0 0 1 0 0 0 8 0 1 0 0 0 0 1 0 0 Phrases: 2 DOC12 $SBUX three reasons starbucks shares might 2015 http://t.co/oZKXEI87ZK $SBUX $AAPL $GOOG $DKDN $$GMCR T# Lev1 Lev0 Phrases: 3 DOC2 Check out custom trade course $AAPL $TWTR peeps T# Lev1 Lev0 1 0 0 0 0 1 0 0 0 0 77 1 0 0 0 0 1 0 0 0 71 1 0 0 0 0 0 0 1 0 69 1 0 0 0 0 0 0 0 1 62 1 0 0 0 0 0 1 0 0 45 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 95 0 0 1 0 0 1 0 0 0 30 0 0 1 0 1 0 0 0 0 87 0 0 0 1 1 0 0 0 0 89 0 0 0 1 0 0 1 0 0 91 0 0 0 1 0 0 0 1 0 92 0 0 0 1 0 1 0 0 0 13 1 0 0 0 1 0 0 0 0 79 1 0 0 0 0 0 0 0 1 15 1 0 0 0 0 0 0 1 0 14 0 1 0 0 0 1 0 0 0 53 1 0 0 0 0 1 0 0 0 87 0 0 1 0 1 0 0 0 0 96 0 0 1 0 0 1 0 0 0 56 0 0 1 0 0 0 1 0 0 Pos 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

  19. AAPL20 Term pTrees(indexed Docs=1,2,3,4; all Pos, Predicate=NotPureZero (np0), strides=10, 1st20 Tweets, 100wds 0 1 2 3 4 5 6 7 8 9 3,2: 0 0 0 0 1 0 0 0 0 0 1,d: 0 0 0 1 0 0 0 0 0 0 1,e: 1 0 0 0 0 0 0 0 0 0 4,6: 0 0 0 0 1 0 0 0 0 0 2,4: 0 1 0 0 0 0 0 0 0 0 2,2: 0 0 0 0 1 0 0 0 0 0 3,3: 0 0 0 1 0 0 0 0 0 0 2,a: 0 0 0 0 0 0 0 0 1 0 2,c: 0 0 0 0 0 0 0 0 0 1 2,9: 0 0 1 0 0 0 0 0 0 0 2,7: 0 1 0 0 0 0 0 0 0 0 2,b: 0 0 0 0 0 0 0 0 1 0 2,5: 0 0 0 0 0 0 0 1 0 0 3,6: 0 1 0 0 0 0 0 0 0 0 2,c: 0 0 0 0 0 1 0 0 0 0 4,7: 0 0 0 0 0 0 1 0 0 0 4,1: 0 0 0 1 0 0 0 0 0 0 2,1: 0 1 0 0 0 0 0 0 0 0 4,a: 0 1 0 0 0 0 0 0 0 0 3,7: 0 0 0 0 0 0 0 0 1 0 1,b: 0 1 0 0 0 0 0 0 0 0 1,a: 0 0 0 1 0 0 0 0 0 0 1,8: 0 1 0 0 0 0 0 0 0 0 3,a: 0 0 1 0 0 0 0 0 0 0 1,6: 0 0 0 0 1 0 0 0 0 0 1,5: 0 0 0 0 0 0 1 0 0 0 3,d: 0 0 0 0 0 0 0 0 0 1 3.b: 0 0 0 0 0 0 0 0 1 0 4,d: 0 0 0 0 0 1 0 0 0 0 4,b: 0 0 0 0 0 0 0 1 0 0 4,3: 0 0 0 0 1 0 0 0 0 0 4,2: 0 0 0 0 1 0 0 0 0 0 3,c: 0 0 0 0 0 0 0 0 1 0 4,8: 0 0 0 0 0 0 0 1 0 0 4,i: 0 0 0 0 0 0 0 0 1 0 4,h: 0 0 1 0 0 0 0 0 0 0 4,j: 0 0 0 0 0 0 0 0 1 0 4,g: 0 0 0 0 0 0 0 1 0 0 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0010000000 0000000100 0000000100 0000000100 0000000100 0000000100 0000001000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000010000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000001 0000000100 0000000100 0000000100 0000000001 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000001000 0000010000 0000000100 0000000100 0000010000 0000000001 0000010000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000010 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000010000 0000000100 0010000000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000010000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000010000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0100000000 0000000100 0000000100 0000000100 0000000100 0000000100 0100000000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000010 0010000000 0000000010 0000000100 0000000010 0000000001 0000000100 0001000000 0000000010 0000000100 0000000010 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0001000000 0000000100 0000000100 0000000100 0010000000 0010000000 0000010000 0000010000 0000010000 0000000100 0000010000 0000000100 0000010000 0010000000 0010000000 0000000100 0000010000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000001 0000000100 0000000100 0000000100 0000000100 0010000000 0000000100 0000100000 0000000100 0000000100 0000000100 0000100000 0000100000 0000000100 0000010000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0100000000 0000000100 0000000100 0000000100 0100000000 0000000100 0100000000 0000000100 0000000100 0000000010 0000100000 0000000100 0000000010 0000000010 0001000000 0000000100 0000000100 0010000000 0000000100 0000000100 0000000100 0000000100 0000000100 0010000000 0000010000 0000010000 0000010000 0010000000 0010000000 0010000000 0000010000 0000000100 0000001000 0000000001 0000000100 0000001000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0100000000 0000000100 0100000000 0000000100 0100000000 0100000000 0000000100 0000000100 0100000000 0000000100 0000000100 0100000000 0100000000 0000100000 0010000000 0001000000 0000000010 0000000010 0000000010 0000000010 0000000100 0000000010 0000100000 0000000100 0000000010 0000000010 0000000010 0000000010 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0010000000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000010000 0010000000 0010000000 0010000000 0010000000 0000010000 0000010000 0010000000 0000010000 0010000000 0010000000 0010000000 0000010000 0000010000 0010000000 0000000100 0000000100 0000000100 0000000100 0000000001 0000000100 0000000100 0000001000 0000001000 0000000100 0000001000 0000001000 0000000100 0000000001 0000001000 0000000100 0000000100 0000000100 0000000100 0000000100 0000000100 0000100000 0000000100 0000000100 0000010000 0000100000 0000000100 0000000100 0000010000 0100000000 0100000000 0000000010 0000000010 0000000100 0000010000 0010000000 0010000000 0000001000 0000001000 2 level  1. 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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 Pos: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

  20. AAPLTerm pTrees(indexed by Documents=5,6,7,8; all Positions, Predicate=NotPureZero (np0), all stride s=10. 1 level  0 1 2 3 4 5 6 7 8 9 5,1: 0 1 0 0 0 0 0 0 0 0 7,1: 0 0 0 0 0 0 0 1 0 0 6,g: 0 0 0 0 0 0 0 0 1 0 6,c: 0 0 0 0 0 0 0 0 1 0 6,b: 0 0 0 0 1 0 0 0 0 0 6,3: 0 0 0 0 0 0 0 0 1 0 6,1: 0 1 0 0 0 0 0 0 0 0 8,a: 0 0 0 0 0 0 0 0 1 0 6,2: 0 0 0 0 1 0 0 0 0 0 6,a: 0 0 0 0 0 0 1 0 0 0 7,b: 0 0 0 0 0 1 0 0 0 0 5,3: 0 1 0 0 0 0 0 0 0 0 8,b: 0 0 0 0 0 1 0 0 0 0 5,2: 0 0 0 0 0 0 0 1 0 0 8,6: 0 0 0 0 1 0 0 0 0 0 5,9: 0 0 0 0 0 0 0 0 0 1 5,a: 0 0 0 0 0 1 0 0 0 0 8,8: 0 0 0 0 0 0 0 0 1 0 6,5: 1 0 0 0 0 0 0 0 0 0 5,6: 0 0 1 0 0 0 0 0 0 0 8,3: 1 0 0 0 0 0 0 0 0 0 8,9: 0 0 0 0 0 0 0 0 1 0 5,7: 0 0 0 0 0 0 0 0 1 0 8,2: 0 0 0 0 0 0 0 1 0 0 5,8: 0 0 0 0 0 0 0 0 1 0 8,7: 0 0 1 0 0 0 0 0 0 0 7,9: 0 0 0 0 0 0 0 0 1 0 7,8: 0 0 0 0 0 0 0 0 1 0 7,7: 0 0 1 0 0 0 0 0 0 0 7,4: 0 1 0 0 0 0 0 0 0 0 8,5: 0 0 0 0 1 0 0 0 0 0 7,3: 0 0 0 0 0 0 0 1 0 0 7,a: 0 0 0 0 0 0 0 0 0 1 6,8: 0 0 0 0 0 0 1 0 0 0 6,j: 0 0 0 0 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