Stock Research Reports Classification Based on Sentiment Analysis

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1 J Wuhan Univ Nat Sci Ed Vol 61 No 2 Apr ~ 130 DOI /j SVM TP 391 A Stock Research Reports Classification Based on Sentiment Analysis PENG Min 1 2 WANG Qing 1 HUANG Jimin 1 ZHOU Li 1 HU Xinhui 1 1 School of ComputerWuhan University Wuhan HubeiChina 2 Shenzhen Institute of Wuhan UniversityShenzhen GuangdongChina AbstractThe stock research report is the important professional investment advice in stock areas Based on web information extraction and retrievalautomatic analysis of the investment advice in massive stock reports will make the significant impact on investors behaviors In this paperwe propose a classification strategy for the stock research report based on sentiment analysis methods Firstlywe extract the integrated features in the stock research report Secondlywe leverage feature selection with the improved CHI statistical methodsand classify the stock research reports through the SVM and Naive Bayes classifiers Finallywe evaluate the classification result considering feature weight feature dimension and sampling number Based on research reports collected from the www eastmoney com the experimental results show thatthe strategy of integrated features selectiondimension reduction as well as training resamplingcan achieve higher performance Key wordssentiment analysisfeature selectionsvmsupport vector machinenaive Bayesimbalanced data stock research report 0 Schu pengm@ whu edu cn

Wuhan UniversityShenzhen 518057GuangdongChina AbstractThe stock research report is the important professional investment advice in stock areas Based on web information extraction and

2 2 125 maker 1 O Hare 2 3 /// Naive Bayes k k- NearestNeighbor Support Vector Machine SVM Pang 5 1 SVM SVM 2 6 SVM Naive Bayes 7 2

3 VSM VSM uni-gram DF IG MI CHI Qiu 9 + CHI + CHI TF( b ) = k m tf( b k d ) i 1 i = 1 tf( b k d i ) b k d i TF( bk ) TF( bk ) 2 2 n-gram 2 3 CHI t k c i χ 2 t k uni-gram c i CHI χ 2 N AD - BC 2 ( t k c ) i = A + C B + D A + B C + D 2

10 6 119 3 874 4 510 97 125 2 6 000 3 000 1 500 TF( bk ) 2 2 n-gram 2 3 CHI t k c

4 2 127 A c i t k B c i t k C c i t k D c i t k CHI χ 2 ( t k c i ) 2 4 χ 2 ( t k c i ) = AD - BC CHI 2 CHI d i D d i = w 1i w 2i w ki 5 w ki k d i CHI CHI FI CI DI CHI TF TF- IDF IDF( tk ) t k TF-IDF 3 χ 2 ( t k c ) i = N AD - BC 2 A + C B + D A + B C + D FI + CI + DI AD - BC > AD - BC 0 FI = TF ( t k c ) i A + C CI = DI = A A + B A A + C S( t ) k m = max 1 χ 2 ( c ) i { t k } 4 w ki = TF( t ) IDF( ki t ) = k TF( t ) lg N ki ( n tk ) 6 N n tk t k 14 TF-IDF IDF TF-CHI m 3 w ki = TF( t ) S( ki t ) = TF( k t ) max{ ki χ 2 ( t 1 k c ) } i TF( t k c i ) t k c i 7 FI t k CI t k DI t S( tk ) 4 k TF TF-IDF t k TF-CHI t k SVM Naive Bayes

CI = DI = A A + B A A + C S( t ) k m = max 1 χ 2 ( c ) i { t k } 4 w ki = TF( t ) IDF( ki t ) = k TF( t ) lg N ki ( n tk + 0 5 + 0 5) 6 N n tk t k 14 TF-IDF IDF TF-CHI m 3

5 Naive Bayes SVM SVM c i Xx 1 x 2 x n P( c i X ) = P ( X c ) i P( c ) i 9 P( X) P( c i X) ( x i y i ) = 1 x i R y i c i P ( X c i ) ( P X) { 1-1} SVM min{ 1 2 w 2 + C n ζi } P( X c ) i = 1 8 i = n p( x k c ) i 10 k = 1 subject to y i [ wx i + b ] 1 - ζ i p ( x k c i ) x k c i P C ζ i ( X c i ) 3 SVM SVM one-against-one one-against-all one-against-one SVM k kk - 1/ SVM 2 one-against-all 15 k k SVM one-against-all 3 4 one-against-one % 20% Naive Bayes Naive Bayes ICTCLAS P Precision R Recall F1 F1 MacroF1 1

c i P C ζ i ( X c i ) 3 SVM SVM 3 1 1 one-against-one one-against-all one-against-one 14 000 600 SVM k kk - 1/ 1 400 12 000 2 SVM 2

6 TF-IDF TF % TF-IDF TF SVM 3 3 % Bayes Macro F1 11 6% F1 10% 20% 40% 60% SVM Naive Bayes Naive Bayes 2SVM SVM 2 Naive Bayes Pang % 20% 40% 60% TF SVM TF 4 F1 10% 20% 40% 50% 60% % % 20% 40% 60% TF TF-IDF TF-CHI SVM 5 5 Macro F1 10% 20% 40% 60% TF TF-IDF TF-CHI TF TF-CHI 40% 4 F1 F % 20% 40% 60% TF SVM Naive 40% SVM F SVM TF 40%

462 0 52 0 613 0 612 0 629 0 755 0 771 0 763 0 774 0 774 4 40% 0 597 0 569 0 490 0 613 0 475 0 303 0 763 0 896 0 874 3 7 600 1 400 1 500 10% 20% 40% 60% TF TF-IDF TF-CHI SVM 5 5 Macro F1 10% 20% 40%

7 Bolón-Canedo VSánchez-Maro no 珘 NAlonso-Betanzos A A review of feature selection methods on synthetic data J Knowledge and Information Systems CHI Qiu L QZhao R YZhou G et al An extensive empirical study of feature selection for text categorizationdb / OL http/ /ieeexplore ieee org /xpls / icp jsparnumber = Schumaker R PZhang YHuang C N et al Evaluating sentiment in financial news articles J Decision Support Systems O hare NDavy MBermingham Aet al Topic-dependent sentiment analysis of financial blogsc/ /Proceedings of the 1st International CIKM Workshop on Topic- Sentiment Analysis for Mass Opinion New YorkACM J Yang L GZhu JTang S P Survey of text sentiment a- nalysis J Journal of Computer Applications Ch 4 J classification on imbalanced data distribution J Journal of Chinese Information Processing Ch 10Drummond CHolte R C C4 5class imbalanceand cost sensitivitywhy under-sampling beats over-sampling C/ /Workshop on Learning from Imbalanced Datasets II WashingtonD CICML Galavotti LSebastiani FSimi M Experiments on the use of feature selection and negative evidence in automated text categorizationc/ /Research and Advanced Technology for Digital Libraries Berlin HeidelbergSpringer- Verlag CHI J Pei Y BLiu X X Study on Improved CHI for feature selection in Chinese text categorization J Computer Engineering and Applications Ch 13 x 2 J Xiong Z YZhang P ZZhang Y F Improved approach to Zhao Y YQin BLiu T Sentiment Analysis J Journal of Software Ch CHI in feature extraction J Journal of Computer Applications Ch 5Pang BLee LVaithyanathan S Thumbs upsenti- 14Debole FSebastiani F Supervised term weighting for ment classification using machine learning techniques automated text categorizationc Text mining and its C/ /Proceedings of the ACL-02 conference on Empirical applications Berlin HeidelbergSpringer-Verlag2004 methods in natural language processing-volume StroudsburgAssociation for Computational Linguistics J Debole FSebastiani F An analysis of the relative hardness of Reuters subsets J Journal of the Ameri Wang Z HWang Z QLi S Set al Feature selection can Society for Information Science and technology2005 for imbalanced sentiment classificationj Journal of Chinese Information Processing Ch J Wang Z QLi S SZhu Q Met al Chinese sentiment

Evaluating sentiment in financial news articles J Decision Support Systems2012533458-464 2O hare NDavy MBermingham Aet al Topic-dependent sentiment analysis of financial blogsc/ /Proceedings of the

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