A New Method for Sentiment Classification in Text Retrieval

R. Dale et al. (Eds.): IJCNLP 2005, LNAI 3651, pp. 1 – 9, 2005. © Springer-Verlag Berlin Heidelberg 2005 A New Method for Sentiment Classification in Text Retrieval Yi Hu1, Jianyong Duan1, Xiaoming Chen1,2, Bingzhen Pei1,2, and Ruzhan Lu1 1 Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China, 200030 2 School of Computer Science and Engineering, Guizhou University, Guiyang, China, 550025 {huyi, duan_jy, chen-xm, peibz, rz-lu}@cs.sjtu.edu.cn Abstract. Traditional text categorization is usually a topic-based task, but a subtle demand on information retrieval is to distinguish between positive and negative view on text topic. In this paper, a new method is explored to solve this problem. Firstly, a batch of Concerned Concepts in the researched domain is predefined. Secondly, the special knowledge representing the positive or negative context of these concepts within sentences is built up. At last, an evaluating function based on the knowledge is defined for sentiment classifica- tion of free text. We introduce some linguistic knowledge in these procedures to make our method effective. As a result, the new method proves better compared with SVM when experimenting on Chinese texts about a certain topic. 1 Introduction Classical technology in text categorization pays much attention to determining whether a text is related to a given topic [1], such as sports and finance. However, as research goes on, a subtle problem focuses on how to classify the semantic orientation of the text. For instance, texts can be for or against “racism”, and not all the texts are bad. There exist two possible semantic orientations: positive and negative (the neutral view is not considered in this paper). Labeling texts by their semantic orientation would provide readers succinct summaries and be great useful in intelligent retrieval of information system. Traditional text categorization algorithms, including Naïve Bayes, ANN, SVM, etc, depend on a feature vector representing a text. They usually utilize words or n-grams as features and construct the weightiness according to their presence/absence or fre- quencies. It is a convenient way to formalize the text for calculation. On the other hand, employing one vector may be unsuitable for sentiment classification. See the following simple sentence in English: － Seen from the history, the great segregation is a pioneering work. Here, “segregation” is very helpful to determine that the text is about the topic of racism, but the terms “great” and “pioneering work” may just be the important hints for semantic orientation (support the racism). These two terms probably contribute 2 Y. Hu et al. less to sentiment classification if they are dispersed into the text vector because the relations between them and “segregation” are lost. Intuitively, these terms can provide more contribution if they are considered as a whole within the sentence. We explore a new idea for sentiment classification by focusing on sentences rather than entire text. “Segregation” is called as Concerned Concept in our work. These Concerned Concepts are always the sensitive nouns or noun phrases in the researched domain such as “race riot”, “color line” …

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