A comparative analysis of text representation, classification and clustering methods over real project proposals  

作　　者：Meltem Aksoy Seda Yanık Mehmet Fatih Amasyali 

机构地区：[1]Industrial Engineering Department,Istanbul Technical University,Istanbul,Turkey [2]Computer Engineering Department,Yıldız Technical University,Istanbul,Turkey

出　　处：《International Journal of Intelligent Computing and Cybernetics》2023年第3期595-628,共34页智能计算与控制论国际期刊（英文）

摘　　要：Purpose-When a large number of project proposals are evaluated to alocate available funds,grouping them based on their simiarites is benefciaL.Current approaches to group proposals are primarily based on manual matching of similar topics,discipline areas and keywordls declared by project applicants.When the number of proposals increases,this task becomes complex and requires excessive time.This paper aims to demonstrate how to ffctively use the rich information in the titles and abstracts of Turkish project propsals to group them atmaially.Design/methodology/approach-This study proposes a model that effectively groups Turkish project proposals by combining word embedding,clustering and classification technigues.The proposed model uses FastText,BERT and term frequency/inverse document frequency(TF/IDF)word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish.The extracted terms were grouped using both the clustering and classification techniques.Natural groups contained within the corpus were discovered using k-means,k-means++,k-medoids and agglomerative clustering algorithms,Additionally,this study employs classification approaches to predict the target class for each document in the corpus.To classify project proposals,var ious classifiers,including k nearest neighbors(KNN),support vector machines(SVM),artificial neural networks(ANN),cassftcation and regression trees(CART)and random forest(RF),are used.Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.Findings-The results show that the generated word embeddings an fftvely represent proposal texts as vectors,and can be used as inputs for dustering or casificatiomn algorithms.Using clustering algorithms,the document corpus is divided into five groups.In adition,the results demonstrate that the proposals can easily be categoried into predefmned categories using cassifiation algorithms.SVM-Linear achieved the highest predicti

关 键 词：Project proposal selection Text mining Word embedding Text clustering Text classification 

分 类 号：O23[理学—运筹学与控制论]