فصل اول پیشگفتار. 1
1-1- مقدمه. 2
1-3- تحلیل احساس در متن. 6
1-4- اهداف رساله. 8
1-5- روش کار. 9
1-6- ساختار پایان نامه. 9
فصل دوم کارهای انجام شده 10
2-1- مقدمه. 11
2-2- تعریف مسئله. 11
2-3- گام اول تحلیل احساس در متن. 12
2-4- روشهای مبتنی بر خصیصههای N-gram.. 13
2-5- الگوریتمهای انتخاب خصیصه. 18
فصل سوم روش پیشنهادی. 22
3-1- پیش گفتار. 23
3-2- منابع مورد نیاز. 23
3-3- روش پیشنهادی اول. 25
3-3-1. پیش پردازش اسناد 26
3-3-2. برچسب گذاری ادات سخن. 29
3-3-3. استخراج بردار خصیصهها و ترکیب خصیصهها 30
3-3-4. اعمال الگوریتم انتخاب خصیصه. 33
3-4- روش پیشنهادی دوم 34
3-5- روش پیشنهادی سوم 37
3-5-1. استخراج پلاریته کلمات و فیلتر بردار خصیصه. 38
فصل چهارم پیاده سازی و نتایج گرفته شده 47
4-1- مقدمه. 48
4-2- مجموعه دادهها 48
4-3- طبقهبندی دادهها 48
4-4- نتایج روش اول. 49
4-5- نتایج روش دوم 52
4-6- نتایج روش سوم 53
4-7- مقایسه روش پیشنهادی با روشهای قبل. 53
8-4- نتایج اعمال روش پیشنهادی برای زبان فارسی..........................................................................................................................54
4-9- کارهای آینده 58
مراجع و منابع. 59
منبع:
[1] A. Abbasi, S. France, Z. Zhang, H. Chen; ” Selecting Attributes for Sentiment Classification Using Feature Relation Networks.”, IEEE Transactions on Knowledge and Data Engineering 23, pp. 447–462 (2011).
[2] A. Ahmed, H. Chen, A. Salem; “Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums,” ACM Trans. Information Systems,vol. 26, no. 3,article no. 12, 2008
[3] A. Abbasi, H. Chen, S. Thoms, T. Fu; “Affect Analysis of WebForums and Blogs Using Correlation Ensembles” IEEE Trans.Knowledge and Data Eng.,vol. 20, no. 9, pp. 1168-1180, Sept. 2008.
[4] B. Pang, L. Lee, S. Vaithyanathan; ”Thumbs up? Sentiment classification using machine learning techniques.”, Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86, (2002).
[5] B. Agarwal, N. Mittal; ”Optimal Feature Selection Methods for Sentiment Analysis”, 14th International Conference on Intelligent Text Processing and Computational Linguistics, Vol-7817, pages-13-24, 2013.
[6] C.E. Shannon; “A Mathematical Theory of Communication,”Bell Systems Technical J.,vol. 27, no. 10, pp. 379-423, 1948.
[7] C. Priyanka, G. Deepa, ” Identifying the Best Feature Combination for Sentiment Analysis of Customer Reviews” International Conference on Advances in Computing, Communications and Informatics (ICACCI),India , pp. 102 – 108, Aug 2013.
[8] C.E. Shannon, “A Mathematical Theory of Communication,”Bell
Systems Technical J.,vol. 27, no. 10, pp. 379-423, 1948.
[9] E.Andrea and S.Fabrizio, "SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining," In Proceedings of the 5th Conference on Language Resources and Evaluation , LREC’06, page 417-422,2006.
[10] E. Riloff, S. Patwardhan, and J. Wiebe, “Feature Subsumption for Opinion Analysis,”Proc. Conf. Empirical Methods in Natural Language Processing,pp. 440-448, 2006.
[11] J.R. Quinlan; “Induction of Decision Trees”, Machine Learning, vol. 1, no. 1, pp. 81-106, 1986.
[12] J. Wiebe, T. Wilson, R. Bruce, M. Bell, and M. Martin; “Learning Subjective Language”, Computational Linguistics,vol. 30, no. 3, pp. 277-308, 2004.
[13] J. Blitzer, M. Dredze, F. Pereira; ”Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification.”, Proceedings of the Association for Computational Linguistics (ACL), pp. 440–447 (2007).
[14] J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack, “Sentiment Analyzer: Extracting Sentiments about a Given Topic Using Natural Language Processing Techniques,”Proc. Third IEEE Int’l Conf. Data Mining,pp. 427-434, 2003.
[15] J.R. Quinlan, “Induction of Decision Trees,” Machine Learning,vol. 1, no. 1, pp. 81-106, 1986.
[16] K. Tsutsumi, K. Shimada, and T. Endo, “Movie Review Classification Based on Multiple Classifier,”Proc. 21st Pacific Asia Conf. Language, Information, and Computation,pp. 481-488, 2007.
[17] L. Bing , Z. Lei “Mining Text Data”, springer, USA , 2012.
[18] L. Yu and H. Liu, “Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution,”Proc. 20th Int’l Conf. Machine Learning,pp. 856-863, 2003.
[19] L. Yu and H. Liu, “Efficient Feature Selection via Analysis of Relevance and Redundancy,”J. Machine Learning Research, vol. 5,pp. 1205-1224, 2004.
[20] M. Gamon; “Sentiment Classification on Customer Feedback Data:Noisy Data, Large Feature Vectors, and the Role of Linguistic Analysis,”Proc. 20th Int’l Conf. Computational Linguistics,pp. 841-847, 2004.
[21] M. Hall, L.A. Smith; “Feature Subset Selection: A Correlation Based Filter Approach,”Proc. Fourth Int’l Conf. Neural Information Processing and Intelligent Information Systems,pp. 855-858, 1997.
[22] M. Ghiassi, J. Skinner, D. Zimbra: “Twitter brand sentiment analysis: A hybrid system usingN-gram analysis and dynamic artificial neural network”, Expert Systems with Applications, 40, (2013) 6266–6282
[23] p. Bo, Lillian Lee, “Opinion Mining and Sentiment Analysis”, Information Retrieval, Vol. 2, Nos. 1–2, pp. 1–135, (2008)
[24] T. Zhang, D. Tao, X. Li, and J. Yang, “Patch Alignment for Dimensionality Reduction,”IEEE Trans. Knowledge and Data Eng., vol. 21, no. 9, pp. 1299-1313, Sept. 2009
[25] V. Ng, S. Dasgupta, S.M.N. Arifin; “Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews”, Conf. Computational Linguistics, Assoc. for Computational Linguistics, pp. 611-618, 2006.
[26] Z. Fei, J. Liu, G. Wu; “Sentiment Classification Using Phrase Patterns”, Proc. Fourth IEEE Int’l Conf. Computer Information Technology,pp. 1147-1152, 2004.
[27] WEKA. Open Source Machine Learning Software Weka, http://www.cs.waikato.ac.nz/ml/weka/