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