Research On Several Key Technical Issues On Fine-Grained Sentiment Classification

People could get more and more information from BBS,BLOG,News reviews and so on,along with the improvement of information processing technology.However huge of unprocessed raw data could not bring enough useful information to us.Because of this,sentiment analysis,which is a novel topic with potential applications,has received great attention recently.In this dissertation,we will focus on the problems in fine-grained sentiment classification including:target identification,relation extraction,opinion word sentiment orientation decision,semi-supervised ontology construction,semisupervised classification and other key technologies in sentiment classification.In document and sentence sentiment classification task,we propose a semisupervised conditional maximum entropy.This algorithm combines entropy regularization framework with maximum entropy.In sentence-level,our approach achieve 78.2%accuracy in MPQA data set,the relative improvement given by semi-supervised technique is 5.2%over the supervised methodIn target identification,an algorithm based on conditional random fields is proposed.It extracts features from context,part-of-speech tags,ontology,and converts target identification into sequence labeling problems.The precision of target identification could achieve 91.17%with this algorithm.In relation extraction task,we propose a method which could be used to convert relation extraction task into sequence labeling problem.This algorithm uses conditional random fields to extract relations with syntactic information,POS tags and other features.Experimental results show that this algorithm achieves 15%relative improvements over the baseline method.In model adaption task,we present a novel technique for maximum a posteriori (MAP) adaptation of Conditional Random Fields Model.Through experimental results,we observe that this technique can effectively adapt a background model to a new domain with a small amount of domain specific labeled data.In target identification task,the relative performance improvement of the adapted model over the background model is 34A weakly supervised algorithm,graph mutual reinforcement based bootstrapping, is proposed to construct ontology.This algorithm extract lexicons with seed words and unlebeled corpus.Finally,a practical system in automotive domain is developed for movie review mining.