Aizawa, A. (2003). An information-theoretic perspective of tf–idf measures. Information Processing &; Management, 39(1), 45-65.
Arun Meena, T. V. Prabhakar (2007). Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis. Lecture Notes in Computer Science, 4425, 573-580.
Bengio, Y., Ducharme, R., Vincent, P., &; Janvin, C. (2003). A neural probabilistic language model. The Journal of Machine Learning Research, 3, 1137-1155.
Blunsom, P., Grefenstette, E., &; Kalchbrenner, N. (2014). A Convolutional Neural Network for Modelling Sentences. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 655-665.
Bullinaria, J. A., &; Levy, J. P. (2007). Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior research methods, 39(3), 510-526.
Bradley, M.M., &; Lang, P.J. (1999). Affective norms for English words (ANEW): Instruction manual and affective ratings. Technical Report C-1, The Center for Research in Psychophysiology, University of Florida.
Cambria, E., Havasi, C., &; Hussain, A. (2012, May). SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis. International Conference of the Florida Artificial Intelligence Research Society, 202-207.
Che, W., Li, Z., &; Liu, T. (2010, August). Ltp: A chinese language technology platform. Proceedings of the 23rd International Conference on Computational Linguistics, 13-16.
Choi, Y., Cardie, C., Riloff, E., &; Patwardhan, S. (2005, October 6–8). Identifying sources of opinions with conditional random fields and extraction patterns. Proceedings of the conference on empirical methods in natural language processing (EMNLP 2005), Vancouver, BC, Canada, 355–362.
Chowdhury, G. G. (2003). Natural language processing. Annual review of information science and technology, 37(1), 51-89.
Church, K. W., &; Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational linguistics, 16(1), 22-29.
Cohen, R., Goldberg, Y., &; Elhadad, M. (2012, July). Domain adaptation of a dependency parser with a class-class selectional preference model. In Proceedings of ACL 2012 Student Research Workshop, 43-48.
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., &; Kuksa, P. (2011). Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12, 2493-2537.
Das, S., &; Chen, M. (2001). Yahoo! for Amazon: Extracting market sentiment from stock message boards. Management Science, 53(9), 1375-1388.
Dave, K., Lawrence, S., &; Pennock, D. M. (2003, May 20–24). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. Proceedings of the 12th international WWW conference, Budapest, Hungary, 519–528.
Ekkekakis, P. (2013). The measurement of affect, mood, and emotion: A guide for health-behavioral research. Cambridge University Press.
Esuli, A., &; Sebastiani, F. (2006, May). Sentiwordnet: A publicly available lexical resource for opinion mining. Proceedings of LREC, 6, 417-422.
Faruqui, M., &; Dyer, C. (2014, June). Community evaluation and exchange of word vectors at wordvectors. org. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics.
Fellbaum, C. (1998). WordNet. Blackwell Publishing Ltd.
Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
Giraudo, H., &; Voga, R.M. (2007). Lexema-based model vs. Morpheme- based model from psycholinguistic perspectives. Comunicación presentada en el congreso Morphology in Tolouse. Tolouse, 108-114.
Hatzivassiloglou, Vasileios and Kathleen R. McKeown, (1997) Predicting the semantic orientation of adjectives. Proceedings of Annual Meeting of the Association for Computational Linguistics.
Hu, M., &; Liu, B. (2004, August). Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 168-177.
Huang, J. R., Hsieh, S. K., Hong, J. F., Chen, Y. Z., Su, I. L., Chen, Y. X., &; Huang, S. W. (2010). Chinese Wordnet: design, implementation, and application of an infrastructure for cross-lingual knowledge processing. Journal of Chinese Information Processing, 24(2), 14-23.
Janyce Wiebe M. (1994). Tracking point of view in narrative. Computational Linguistics, 20(2), 233-287.
Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Springer Berlin Heidelberg.
Jones, S.K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of documentation, 28(1), 11-21.
Kim, Y. (2014). Convolutional neural networks for sentence classification. Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), 1746-1751, arXiv preprint arXiv:1408.5882.
Kim, J., Li, J. J., &; Lee, J. H. (2009, August). Discovering the discriminative views: measuring term weights for sentiment analysis. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1-Volume 1, Association for Computational Linguistics, 253-261.
Ko, Y. (2012, August). A study of term weighting schemes using class information for text classification. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 1029-1030.
Kontopoulos, E., Berberidis, C., Dergiades, T., &; Bassiliades, N. (2013). Ontology-based sentiment analysis of twitter posts. Expert systems with applications, 40(10), 4065-4074.
Ku, L.W. &; Chen, H.H. (2007). Mining Opinions from the Web: Beyond Relevance Retrieval. Journal of American Society for Information Science and Technology, Special Issue on Mining Web Resources for Enhancing Information Retrieval, 58(12), 1838-1850.
Ku, L. W., Huang, T. H., &; Chen, H. H. (2009, August). Using morphological and syntactic structures for Chinese opinion analysis. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 3, 1260-1269.
Ku, L. W., Huang, T. H., &; Chen, H. H. (2009). Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 3, 1260-1269.
LeCun, Y., Bottou, L., Bengio, Y., &; Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Levy, O., &; Goldberg, Y. (2014). Dependencybased word embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2, 302-308.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.
Liu, B., Hu, M., &; Cheng, J. (2005, May). Opinion observer: analyzing and comparing opinions on the web. Proceedings of the 14th international conference on World Wide Web, 342-351.
Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of natural language processing, 2nd edition.
Lu, B., Song, Y., Zhang, X., &; Tsou, B. K. (2010). Learning Chinese polarity lexicons by integration of graph models and morphological features. Information retrieval technology, 466-477.
Loper, E., &; Bird, S. (2002, July). NLTK: The natural language toolkit. Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics, 1, 63-70.
Ma, Wei-Yun and Keh-Jiann Chen, 2003, Introduction to CKIP Chinese Word Segmentation System for the First International Chinese Word Segmentation Bakeoff, Proceedings of ACL, Second SIGHAN Workshop on Chinese Language Processing, 168-171.
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., &; Potts, C. (2011, June). Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for Computational Linguistics, pp. 142-150.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., &; Dean, J. (2013a). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, 3111-3119.
Mikolov, T., Chen, K., Corrado, G. S., and Dean, D.( 2013b). Efficient estimation of word representations in vector space. CoRR, abs/1301.3781.
Montejo-Ráez, A., Martínez-Cámara, E., Martín-Valdivia, M. T., &; Ureña-López, L. A. (2014). Ranked wordnet graph for sentiment polarity classification in twitter. Computer Speech &; Language, 28(1), 93-107.
Nakagawa, T., Inui, K., &; Kurohashi, S. (2010, June). Dependency tree-based sentiment classification using CRFs with hidden variables. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 786-794.
Paltoglou, G., &; Thelwall, M. (2010, July). A study of information retrieval weighting schemes for sentiment analysis. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 1386-1395.
Pang, B., &; Lee, L. (2005, June). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, 115-124.
Pang, B., &; Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2), 1-135.
Pang, B., Lee, L., &; Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. Proceedings of the ACL-02 conference on Empirical methods in natural language processing, 10, 79-86.
Pang, B., &; Lee, L. (2004, July 21–26). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. Proceedings of the 42nd annual meeting of the Association for Computational Linguistics (ACL) Barcelona, Spain, 271–278.
Pang, B., &; Lee, L. (2005, June). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp. 115-124). Association for Computational Linguistics.
Peng, F., Feng, F., &; McCallum, A. (2004, August). Chinese segmentation and new word detection using conditional random fields. Proceedings of the 20th international conference on Computational Linguistics,562.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et. al. &; Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825-2830.
Quan, C., &; Ren, F. (2014). Unsupervised product feature extraction for feature-oriented opinion determination. Information Sciences, 272, 16-28.
Riloff, E., &; Wiebe, J. (2003, July). Learning extraction patterns for subjective expressions. Proceedings of the 2003 conference on Empirical methods in natural language processing, 105-112.
ŘEHŮŘEK, Radim and Petr SOJKA. (2010) Software Framework for Topic Modelling with Large Corpora. In Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks. Valletta, 46-50.
Salton, G., Wong, A., &; Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613-620.
Santos, C.N. dos, &; Gatti, M. (2014). Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of the 25th International Conference on Computational Linguistics (COLING), Dublin, Ireland, 69-78.
Sears, A., &; Jacko, J. A. (Eds.). (2007). The human-computer interaction handbook: fundamentals, evolving technologies and emerging applications. CRC press.
Stone, P. J., &; Hunt, E. B. (1963, May). A computer approach to content analysis: studies using the general inquirer system. Proceedings of the May 21-23, 1963, spring joint computer conference, 241-256.
Strapparava, C., &; Valitutti, A. (2004, May). WordNet Affect: an Affective Extension of WordNet. Proceedings of the 4th International Conference on Language Resources and Evaluation, 4, 1083-1086.
Su, Q., Xu, X., Guo, H., Guo, Z., Wu, X., Zhang, X., Swen, B., 2008. Hidden sentiment association in Chinese web opinion mining. In: Proceedings of the 17th international conference on World Wide Web, pp. 959-968.
Sun, Y. T., Chen, C. L., Liu, C. C., Liu, C. L., &; Soo, V. W. (2010). Sentiment Classification of Short Chinese Sentences. Proceedings of the 22nd Conference on Computational Linguistics and Speech Processing (ROCLING 2010), 184-198. (in Chinese)
Tsai, A.C.R., Wu, C.E., Tsai, R.T.H., &; Hsu, J.Y.J. (2013). Building a concept-level sentiment dictionary based on commonsense knowledge. IEEE Intelligent Systems, (2), 22-30.
Tan, J.S.F., Lu, E.H.C., &; Tseng, V.S. (2013). Preference-oriented mining techniques for location-based store search. Knowledge and Information Systems, 34(1), 147–169.
Tan, S., &; Zhang, J. (2008). An empirical study of sentiment analysis for Chinese documents. Expert Systems with Applications, 34(4), 2622-2629.
Turney, Peter D. (2002) Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2002), 417-424.
Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer. New York.
Van Rijsbergen, C. J. (1979). Information Retrieval. 2ed. London: Butterworth.
Wiebe, Janyce, Rebecca F. Bruce, and Thomas P. O'Hara, (1999) Development and use of a gold-standard data set for subjectivity classifications. Proceedings of the Association for Computational Linguistics (ACL-1999)..
Wan, X. (2009, August). Co-training for cross-lingual sentiment classification. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 1, 235-243.
Wang, X., Zhao, Y., &; Fu, G. (2011). A Morpheme-based Method to Chinese Sentence-Level Sentiment Classification. International Journal of Asian Language Processing, 21(3), 95-106.
Wu, Y., &; Wen, M. (2010, August). Disambiguating dynamic sentiment ambiguous adjectives. Proceedings of the 23rd International Conference on Computational Linguistics, 1191-1199.
Wu, H. C., Luk, R. W. P., Wong, K. F., &; Kwok, K. L. (2008). Interpreting tf-idf term weights as making relevance decisions. ACM Transactions on Information Systems (TOIS), 26(3), 13:1-13:37.
Wilson, T., Wiebe, J., &; Hoffmann, P. (2005, October). Recognizing contextual polarity in phrase-level sentiment analysis. Proceedings of the conference on human language technology and empirical methods in natural language processing, 347-354.
Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., ... &; Patwardhan, S. (2005, October). OpinionFinder: A system for subjectivity analysis. Proceedings of hlt/emnlp on interactive demonstrations, 34-35.
Wu, Z., &; Tseng, G. (1993). Chinese text segmentation for text retrieval: Achievements and problems. Journal of the American Society for Information Science, 44(9), 532-542.
Xu, G., Huang, C.R., Wang, H., 2013. Extracting Chinese product features: representing a sequence by a set of skip-bigrams. In: Chinese Lexical Semantics, 72-83.
Yang, H.L., Chao F.Y.C., (2014), Sentiment analysis for Chinese reviews of movies in multi-genre based on morpheme-based features and collocations, Information Systems Frontiers, http://dx.doi.org/10.1007/s10796-014-9498-1.
Yu, H. C., Huang, T. H. K., &; Chen, H. H. (2012). Domain Dependent Word Polarity Analysis for Sentiment Classification. Computational Linguistics and Chinese Language Processing ROCLING XXIV, 17(4), 33-48.
Zhang, W., Xu, H., &; Wan, W. (2012). Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications, 39(11), 10283-10291.
Zhang, C., Zeng, D., Li, J., Wang, F.Y., &; Zuo, W. (2009). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 60(12), 2474-2487.
Zhang, H., Yu, Z., Xu, M., Shi, Y., 2011. Feature-level sentiment analysis for Chinese product reviews. International Conference on Computer Research and Development ICCRD, 2, pp. 135-140.
Zhou, S., &; Mondragón, R. J. (2004). The rich-club phenomenon in the Internet topology. Communications Letters, IEEE, 8(3), 180-182.
吳孟淞與王新民(2011) 運用詞關聯轉化語言模型於新聞文件分類之研究,第十七屆資訊管理暨實務研討會,高雄。
黃居仁(2005) 漢字知識表達的幾個層面:字、詞與詞義關係概論,漢字與全球化國際學術研討會,臺北。
高照明(2010) 中文詞彙語意資料的整合及擷取:詞彙語意學的觀點,高照明編著,計算語言學論文集, 68-97。
周亞民,黃居仁 (2005)漢字意符知識結構的建立,第六届汉语词汇语义学研讨会论文集。
洪嘉馡,黃居仁,許銘維(2013) 以中文十億詞語料庫為基礎之兩岸詞彙對比研究,中文計算語言學期刊,8(2),19-34.周亞民,吳玲玲,黃居仁(2005) 漢字知識本體-以字為本的知識架構與其應用示例,國立台灣大學資訊管理學系,未發表博士論文。趙逢毅,鍾曉芳 (2011) 基於辭典詞彙釋義之多階層釋義關聯程度計量─ 以 [目] 字部為例,中文計算語言學期刊,16(3-4),21-39。
趙元任(2002) 中國語文法,香港中文大學,丁邦新譯,2002增訂版。
何永清(2005) 現代漢語語法新探,臺北:臺灣商務印書館。梅家駒,竺一鳴,高蘊琦,殷鴻翔(1984) 同義詞詞林,香港:商務印書館。
林語堂(1955) 整理漢字草案,中國世紀, 14-15。
唐蘭(1935) 古文字學導論。
詞庫小組(1993) 技術報告93-05:中文詞類分析(三版),中央研究院。