This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences . Chinese words are classified into eight morphological types by two proposed classifiers , CRF classifier and SVM classifier . Experiments show that the injection of morphological information improves the performance of the word polarity detection . To utilize syntactic structures , we annotate structural trios to represent relations between sentence segments . Experiments show that considering structural trios is useful for sentence opinion analysis . The best f-score achieves 0.77 for opinion word extraction , 0.62 for opinion word polarity detection , 0.80 for opinion sentence extraction , and 0.54 for opinion sentence polarity detection . Sentiment analysis has attracted much attention in recent years because a large scale of subjective information is disseminated through various platforms on the web . Sentiment information can be applied to a wide variety of fields , including product recommendation , review summarization , public polling , and so on . Opinion dictionaries are important resources for identifying subjective information . Several approaches were proposed to collect such resources . Wiebe ( 2000 ) learned subjective adjectives from corpora . Takamura et al . ( 2005 ) extracted semantic orientations of words . Ku et al . ( 2007 ) measured sentiment degrees of Chinese words by averaging the sentiment scores of the composing characters . When the opinion words are available , the polarities of sentences and documents can be determined by them . Riloff and Wiebe ( 2003 ) learned the extraction patterns for subjective expressions . Kim and Hovy ( 2004 ) found the polarity of subjective expressions . Pang et al . ( 2002 ) and Dave et al . ( 2003 ) explored various techniques at document level . Morphological information has been widely used in classifying words , telling the meanings , and doing other in-depth analysis ( Tzeng and Chen , 2002 ) . However , morphological information was seldom applied either in Chinese opinion extraction , or in solving the coverage problem of opinion dictionary . Instead of bag-ofcharacters approach ( Ku et al . , 2007 ) , this paper employs morphological structures of words to extract opinion words . Relations between sentence segments are also defined by linguistics in the Chinese language . These are similar to morphological structures between Chinese characters . Based on parsing trees of sentences , we identify these relations and utilize them for opinion analysis on sentences . As the experimental corpus , some researchers managed to generate annotated materials and gold standards under many constraints . Ku set a standard for generating final answers from annotations of multiple annotators ( Ku et al . , 2007 ) , and Somasundaran annotated discourse information from meeting dialogs to train a sentiment model ( Somasundaran et al . , 2007 ) . For multilingual issues , researchers concerned mainly about the applicability of corpus and algorithms from the native language to foreign languages ( Banea et al . , 2008 ; Bautin et al . , 2008 ) . Several opinion analysis systems have been developed so far . OASYS ( Cesarano et al . , 2007 ) and CopeOpi ( Ku et al . , 2007 ) allow users input their queries and select preferred data sources , and then track opinions in a time zone . For both systems , extracting opinions is the main focus , while holders and targets are identified implicitly when retrieving relevant documents . Carenini 's team proposed a graphical user interface for evaluative texts ( 2006 ) , in which color blocks were used to present the evaluations for components of products . Fair News Reader , a Japanese news Web system , incorporates sentiment information insensibly in an interesting way ( Kawai et al . , 2007 ) . It provides readers " balanced " reports by analyzing the sentiment in news articles which readers have read , and suggests them new articles according to the analysis results . It leads the application of opinion analysis to the direction of personalization . This paper considers morphological and syntactic structures in analyzing Chinese opinion words and sentences . For morphological structures , eight Chinese morphological types are defined . CRF classifier and SVM classifier for morphological type classification are proposed . Experiments show that CRF classifier achieves the best accuracy 0.70 in type classification , which is 8 % better than SVM classifier . We further show that word morphological structures benefit the opinion word extraction significantly . With the help of the sentiment dictionary NTUSD , the f-score of opinion word extraction achieves 0.77 and the f-score of the word polarity detection achieves 0.62 when the word morphological types are provided by the SVM classifier . They are comparably better than bag-of-character approach and the dictionary based approach . We defined structural trios to represent the relations between sentence segments and also extract these relations using CRF algorithm . Results show that considering structural trios benefits the opinion analysis on sentences . An fscore 0.80 for opinion extraction and an f-score 0.54 for polarity detection are achieved , which is a great improvement . The opinion scoring functions for morphological types and structural trios are critical for polarity detection , and scoring functions for words determine the scoring functions for sentences . Now we define these functions intuitively based on linguistic rules , but learning methods like regression will be investigated in the future . Examining the interaction of cues from word and sentence levels on the opinion sentence extraction and the opinion polarity detection is our next goal .

Challenge: There is not much work on applying morphological information in opinion extraction in Chinese.
Approach: They propose to utilize morphological and syntactic features for Chinese opinion analysis on word and sentence levels.
Outcome: They show that using morphological structures helps opinion analysis in Chinese, outperforming the existing bat-of-character approach and the dictionary-based approach.