|
|
- 2018
基于保序子矩阵和频繁序列模式挖掘的文本情感特征提取方法
|
Abstract:
摘要： 特征提取是进行文本情感分析的关键步骤之一,是影响其结果好坏的主要因素。针对网络评论语句中表达形式多变的特点,结合语义相似度计算得到近义词TF-IDF(term frequency—inverse document frequency)权重向量;根据评论语句长短不一的特点,基于OPSM(order-preserving submatrix)双聚类算法挖掘出权重向量中的局部模式;使用改进的PrefixSpan算法挖掘分类频繁短语特征,这类特征能有效利用词语的顺序信息,同时也通过词语间隔等限制来提升频繁短语区分情感倾向的能力。最后将该方法用于处理商品评论语料,并进行情感分析任务实验,结果表明所提取的文本特征效果有较大的提升。
Abstract: Feature extraction is one of the key steps in text sentiment analysis, which is also the main factor that affects the result. According to the variant expression of online review, the synonyms TF-IDF(term frequency-inverse document frequency)weight vector is obtained based on the semantic similarity. Then in view of the different length of online review, the local patterns of the feature vectors are identified with OPSM(order-preserving submatrix)biclustering algorithm. We improve PrefixSpan algorithm to detect the frequent classification phrase feature, which contain word order information. Furthermore some important factors, such as the separation of word, are also employed to improve the discriminative ability of sentiment orientation. Finally, the proposed method is applied to the sentiment analysis task experiment of the product reviews, and the results show that the text feature extraction has a better performance
| [1] | LIU Zhiwen, XUE Yue, LI Meihang, et al. Discovery of deep order-preserving submatrix in DNA microarray data based on sequential pattern mining[J]. International Journal of Data Mining & Bioinformatics, 2017, 17(3):217-237. |
| [2] | MNIH A, HINTON G E. A scalable hierarchical distributed language model[C] // Proceedings of the 21st International Conference on Neural Information Processing Systems(NIPS'08).[S.l.] : Curran Associates Inc, 2008: 1081-1088. |
| [3] | KAUFMAN L, ROUSSEEUW P J. Finding groups in data: an introduction to cluster analysis[M]. New York: John Wiley & Sons, 2009. |
| [4] | WANG Hui. All common subsequences[C] // Proceedings of the International Joint Conference on Artificial Intelligence. Freiburg: IJCAI-INT, 2007: 635-640. |
| [5] | ZHANG Huaping, YU Hongkui, XIONG Deyi, et al. HHMM-based chinese lexical analyzer ICTCLAS[C] // Sighan Workshop on Chinese Language Processing. Stroudsburg: Association for Computational Linguistics, 2003: 758-759. |
| [6] | PANG Bo, LEE L, VAITHYANATHAN S. Thumbs up? Sentiment classification using machine learning techniques[C] // Proceedings of 2002 Conference on Empirical Methods in Natural Language Processing. Somerset: ACL, 2002: 79-86. |
| [7] | BOJANOWCKI P, GRAVE E, JOULIN A, et al. Enriching word vectors with subword information[EB/OL].[2017-03-15].http://arxiv.org/abs/1607.04606. |
| [8] | SIVIC J, ZISSERMAN A. Efficient visual search of videos cast as text retrieval[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(4): 591-606. |
| [9] | ZELLIG S. H. Distributional structure [J]. Word, 1954, 10(2-3):146-162. |
| [10] | PEI Jian, HAN Jiawei, MORTAZAVI-ASL B, et al. Mining sequential patterns by pattern-growth: the prefixspan approach[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(11):1424-1440. |
| [11] | MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. Computer Science, 2013. arXiv:1301.3781v3. |
| [12] | KRIEGEL H P, ZIMEK A. Clustering high-dimensional data:a survey on subspace clustering, pattern-based clustering,and correlation clustering[J]. ACM Transactions on Knowledge Discovery from Data, 2009, 3(1):1-58. |
| [13] | TAN Songbo. ChnSentiCorp[DB/OL].[2010-06-29]. http://www.nlpir.org/?action-viewnews-itemid-77. |
| [14] | T？R？NEN P, KOLEHMAINEN M, WONG G, et al. Analysis of gene expression data using self-organizing maps[J]. Febs Letters, 1999, 451(2):142-146. |
| [15] | KANG S H, SANDBERG B, YIP A M. A regularized k-means and multiphase scale segmentation[J]. Inverse Problems & Imaging, 2017, 5(2):407-429. |
| [16] | CHENG Yinong, CHURCH G M. Biclustering of expression data[C] // Proceedings of International Society for Computational Biology.[S.l.] : AAAI Press, 2000: 93-103. |
| [17] | MATSUMOTO S, TAKAMURA H, OKUMURA M. Sentiment classification using word sub-sequences and dependency sub-trees[C] // Proceedings of the 9th Pacific/Asia Conference on Knowledge Discovery and Data Mining. Berlin: Springer-Verlag, 2005: 301-311. |
| [18] | TAN Songbo, ZHANG Jin. An empirical study of sentiment analysis for chinese documents[J]. Expert Systems with Applications, 2008, 34(4):2622-2629. |
| [19] | TAI Kaisheng, SOCHER R, MANNING C D. Improved semantic representations from tree-structured long short-term memory networks[J]. Computer Science, 2015, 5(1):36. |
| [20] | LAZZERONI L C, OWEN A. Plaid models for gene expression data[J]. Statistica Sinica, 2002: 61-86. |
| [21] | ZHANG Huaping. ICTCLAS[CP/OL].[2017-03-14]. http://ictclas.nlpir.org/. |
| [22] | PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al. Scikit-learn: machine learning in Python[J]. Journal of Machine Learning Research, 2012, 12(10):2825-2830. |
| [23] | BEN-DOR A, CHOR B, KARP R, et al. Discovering local structure in gene expression data: the order-preserving submatrix problem[C] // Proceedings of the 6th Annual International Conference on Computational Biology(RECOMB '02). New York: ACM, 2002: 49-57. |
| [24] | SALTON G, YU C. On the construction of effective vocabularies for information retrieval[J]. SIGPLAN Notices, 1975, 10(1):48-60. |
| [25] | BENGIO Y, DUCHARME R, VINCENT Pascal, et al. A neural probabilistic language model[J]. Journal of Machine Learning Research, 2003, 3(6):1137-1155. |
| [26] | LIU Yiqun, CHEN Fei, KONG Weize, et al. Identifying web spam with the wisdom of the crowds[J]. ACM Transactions on the Web, 2012, 6(1):1-30. |
