# Tweets Miner for Stock Market Analysis

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2 The set of tweets, which includes the set F looks like TW F = { w j }. (4) = { tw F tw ; r 1,... m}. (5) kw F r r = The ratio of the number of transactions, which include the set F, to the total number of transactions is called a support of F basket and it is marked as Supp (F) : kw TWF Supp ( F) =. (6) tw TW A set is called a frequent itemset, if its support value is more than the minimum support that is specified by a user Given the condition (7), we find a set of frequent itemsets Supp ( F) > Supp. (7) min L = F Supp( F ) Supp }. (8) { j j > min Based on frequent itemsets, we can build association rules, which are considered as X Y, (9) where X is called antecedent and Y is called consequent. The objects of antecedent and consequent are the subsets of the frequent set F of considered keywords X Y = F. (10) Software package for tweets mining Let us consider the software package for tweets mining, we developed it using R language and appropriate R packages. R GUI environment with working software is presented in the Fig.1. This software allows users to load tweets from a specified list of twitter users, such as: CNN, WSJ, Reuters, Bloomberg, etc., and perform data mining analysis, comparing results with the stock market chart. This package is described and can be loaded in our analytics blog at Fig.1 Tweets Miner in the R GUI 2

4 Fig.3 The search of frequent terms list and the terms cloud. Fig.4 The cloud of keywords, which are associated with The keyword market" in the loaded tweets. Fig.5 The time dynamics of frequent sets of keywords {apple,stock} and the stock chart for AAPL. 4

5 To compare the time dynamics of frequent sets of keywords, they can be plotted as a standard stocks chart with candles and volumes (Fig.6). Fig.6 The stock chart with candles and volumes and moving averages of keywords frequent sets. The developed package can also plot a crosscorrelation function, which shows the correlation between the moving averages of frequent sets and the stock price Fig.7). It allows to find predictive frequent sets. Fig.7 The crosscorellation between the stock chart AAPL and the moving averages of keywords frequent sets {apple,stock}. 5

6 This software also allows to perform the forecasting for the dynamics of stock charts; it is based on the ARIMA model using R package 'forecast' (Fig.8). Fig.8 The forecasting of the stock chart AAPL using ARIMA model. Consideration and conclusions Comparing the moving averages of some found frequent sets of keywords and the stock market, we can notice that these curves have similar trends regions, which are also proved by the calculated crosscorelation function. We have also performed an additional analysis, which is not included into the package under consideration. In the Fig.9, we showed the graph for the frequent itemsets, which is formed in the analyzed tweets array with some specified keywords. In the Fig.10 we showed the graph of association rules which are formed with the specified keywords. Such graphs can map the semantic structure of analyzed tweets array. Fig.9 The graph for the frequent sets of specified keywords. 6

7 Fig.10 The graph for the association rules of specified keywords. We conducted the Granger test to determine the causation between the time dynamics of frequent itemsets and the stock price. In the first test, we considered the null hypothesis about lack of causality between the dynamics of the frequent itemset {apple, stock} and AAPL stock price; in the second test, we examined the null hypothesis about lack of the causality between AAPL stock prices and the dynamics of the frequent itemset {apple, stock}. The calculations were performed using R packages. We have got the following results: test 1 Granger causality test Model 1: V3 ~ Lags(V3, 1:1) + Lags(V2, 1:1) Model 2: V3 ~ Lags(V3, 1:1) Res.Df Df F Pr(>F) ** --- Signif. codes: 0 *** ** 0.01 * test 2 Granger causality test Model 1: V2 ~ Lags(V2, 1:1) + Lags(V3, 1:1) Model 2: V2 ~ Lags(V2, 1:1) Res.Df Df F Pr(>F) p-value in the first test is equal to , this is significantly less than the standard significance level of The P-value in the second test is equal to , this is substantially more than the standard significance level of It means that the dynamics of the frequent 7

8 itemsets of keywords {apple, stock} in users' tweets under analysis determines the dynamics of Apple stock prices. Thus, the dynamics of support of some of the identified frequent itemsets of keywords reflects the trends of the stock market. Such frequent itemsets can be considered as potential predictive markers. Our next step is going to be the use of multivariate forecasting algorithms, based on the vector autoregressive model (VAR model). These algorithms can include many time series into analyses, the time series describe both stock prices and quantitative characteristics of tweets. We suppose such an approach will give the narrower and more precise forecasting. We are also planning to use the theory of semantic fields, frequent sets, association rules, Galois lattice, and the formal concepts analysis. References [1] Kwak, H., Lee, C., Park, H., & Moon, S. (2010, April). What is Twitter, a social network or a news media?. In Proceedings of the 19th international conference on World wide web (pp ). ACM. [2] Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. P. (2010, May). Measuring user influence in twitter: The million follower fallacy. In 4th international aaai conference on weblogs and social media (icwsm) (Vol. 14, No. 1, p. 8). [3] Benevenuto, F., Rodrigues, T., Cha, M., & Almeida, V. (2009, November). Characterizing user behavior in online social networks. In Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference (pp ). ACM. [4] Pak, A., & Paroubek, P. (2010, May). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of LREC, pp (Vol. 2010). [5] Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1),

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