2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC)最新文献_第5页

Combining heterogeneous features for time series prediction 结合异构特征进行时间序列预测

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-07-01 DOI: 10.1109/BESC.2017.8256383

Charles Chu, J. Brownlow, Qinxue Meng, Bin Fu, Ben Culbert, Min Zhu, Guandong Xu, Xue-zhong He

引用次数: 0

Towards simplified insurance application via sparse questionnaire optimization 基于稀疏问卷优化的简化保险申请

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-07-01 DOI: 10.1109/BESC.2017.8256362

S. Liu, Guandong Xu, Xiao Zhu, Zili Zhou

引用次数: 2

Latent factor analysis for low-dimensional implicit preference prediction 低维内隐偏好预测的潜在因子分析

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-07-01 DOI: 10.1109/BESC.2017.8256380

Zili Zhou, Guandong Xu, Xiao Zhu, S. Liu

引用次数: 0

Sentiment classification of short text using sentimental context 基于情感语境的短文本情感分类

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-07-01 DOI: 10.1109/BESC.2017.8256405

Wenjie Zheng, Zenan Xu, Yanghui Rao, Haoran Xie, Fu Lee Wang, Reggie Kwan

{"title":"Sentiment classification of short text using sentimental context","authors":"Wenjie Zheng, Zenan Xu, Yanghui Rao, Haoran Xie, Fu Lee Wang, Reggie Kwan","doi":"10.1109/BESC.2017.8256405","DOIUrl":"https://doi.org/10.1109/BESC.2017.8256405","url":null,"abstract":"Sentiment analysis has important applications in many areas, including marketing, recommendation, and financial analysis. Since topic modeling can discover hidden semantic structures, researchers put forward sentiment analysis models based on topic models. These models have been successfully applied on long texts, but analysis for short text is a challenging task because of the sparsity of features in short texts. We observe that the textual context has been widely considered on text analysis task, but on sentiment analysis area, most sentiment analysis models still lack of consideration and integration of sentimental context. Thus, by taking the speciality of sentiment analysis task and short text into consideration, we propose the sentimental context to enrich the characteristics and improve the performance of sentiment classification over short text. We first put forward the concept of sentimental context, which is extracted from the text body and sentiment lexicon, and then we integrate the sentimental context and propose two sentiment classification models based on word-level and topic-level respectively. We present results on real-world datasets from various sources, validating the effectiveness of the proposed models.","PeriodicalId":142098,"journal":{"name":"2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC)","volume":"11 22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129989386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Churn prediction model for effective gym customer retention 有效的健身房客户保留率流失预测模型

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 1900-01-01 DOI: 10.1109/BESC.2017.8256385

Jas Semrl, Alexandru Matei

引用次数: 8