SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1872004

Dipankar Das, Sivaji Bandyopadhyay

{"title":"Extracting emotion topics from blog sentences: use of voting from multi-engine supervised classifiers","authors":"Dipankar Das, Sivaji Bandyopadhyay","doi":"10.1145/1871985.1872004","DOIUrl":"https://doi.org/10.1145/1871985.1872004","url":null,"abstract":"This paper presents a supervised multi-engine classifier approach followed by voting to identify emotion topic(s) from English blog sentences. Manual annotation of the English blog sentences in the training set has shown a satisfactory agreement with kappa (κ) measure of 0.85 and MASI (Measure of Agreement on Set-valued Items) measure of 0.82 for emotion topic spans. The baseline system based on object related dependency relations includes the topic oriented thematic roles present in the verb based syntactic frame of the sentences. In contrast, the supervised approach consists of three classifiers, Conditional Random Field (CRF), Support Vector Machine (SVM) and a Fuzzy Classifier (FC). The important features are incorporated based on the ablation study of all features and Information Gain Based Pruning (IGBP) on the development set. One or more emotion topics associated with focused target span are identified based on the majority voting of the classifiers. The supervised multi-engine classifier system has been evaluated with average F-scores of 70.51% and 90.44% for emotion topic and target span identification respectively on 500 gold standard test sentences and has outperformed the baseline system.","PeriodicalId":244822,"journal":{"name":"SMUC '10","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128788034","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: 17

A weighted tag similarity measure based on a collaborative weight model 基于协同权重模型的加权标签相似度度量

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1871999

Gokavarapu Srinivas, Niket Tandon, Vasudeva Varma

{"title":"A weighted tag similarity measure based on a collaborative weight model","authors":"Gokavarapu Srinivas, Niket Tandon, Vasudeva Varma","doi":"10.1145/1871985.1871999","DOIUrl":"https://doi.org/10.1145/1871985.1871999","url":null,"abstract":"The problem of measuring semantic relatedness between social tags remains largely open. Given the structure of social bookmarking systems, similarity measures need to be addressed from a social bookmarking systems perspective. We address the fundamental problem of weight model for tags over which every similarity measure is based. We propose a weight model for tagging systems that considers the user dimension unlike existing measures based on tag frequency. Visual analysis of tag clouds depicts that the proposed model provides intuitively better scores for weights than tag frequency. We also propose weighted similarity model that is conceptually different from the contemporary frequency based similarity measures. Based on the weighted similarity model, we present weighted variations of several existing measures like Dice and Cosine similarity measures. We evaluate the proposed similarity model using Spearman's correlation coefficient, with WordNet as the gold standard. Our method achieves 20% improvement over the traditional similarity measures like dice and cosine similarity and also over the most recent tag similarity measures like mutual information with distributional aggregation. Finally, we show the practical effectiveness of the proposed weighted similarity measures by performing search over tagged documents using Social SimRank over a large real world dataset.","PeriodicalId":244822,"journal":{"name":"SMUC '10","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128375446","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: 26

How to interpret the helpfulness of online product reviews: bridging the needs between customers and designers 如何诠释在线产品评论的帮助:架起顾客和设计师之间的桥梁

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1872000

Jian Jin, Ying Liu

{"title":"How to interpret the helpfulness of online product reviews: bridging the needs between customers and designers","authors":"Jian Jin, Ying Liu","doi":"10.1145/1871985.1872000","DOIUrl":"https://doi.org/10.1145/1871985.1872000","url":null,"abstract":"Helpful reviews are the valuable voice of the customer which benefit both consumers and product designers. On e-commerce websites, consumers are usually encouraged to rate whether a review is helpful or not. As consumers are not obligated to vote reviews, usually only a small proportion of product reviews eventually receive a voting. Also, existing evaluation methods that only use the review voting ratio from customers as the helpfulness are often not consistent with the designers' rating on reviews in interpreting customer needs and preferences. Thus, in this paper, the focus is on how to automatically build the connection between online customer's voting and designer's rating and predict the customer reviews' helpfulness based on the review content. We start the study by building a mapping to express product designers' rating using online helpfulness voting. Further, we propose to utilize regression algorithm to predict the online review's helpfulness with the help of several categories of features extracted from review content. Our experimental study, using a large amount of review data crawled from Amazon and real ratings from product designers confirms the effectiveness of our proposal and shows some very promising results.","PeriodicalId":244822,"journal":{"name":"SMUC '10","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127599625","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: 30

Classifying latent user attributes in twitter 分类twitter中的潜在用户属性

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1871993

D. Rao, David Yarowsky, Abhishek Shreevats, Manaswi Gupta

引用次数: 686

Characterization of the twitter @replies network: are user ties social or topical? twitter @回复网络的特征:用户关系是社交的还是话题性的?

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1871996

Daniel Sousa, L. Sarmento, E. M. Rodrigues

引用次数: 86

A formal study of classification techniques on entity discovery and their application to opinion mining 实体发现分类技术及其在意见挖掘中的应用研究

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1871992

Shadi Banitaan, Saeed Salem, Wei Jin, Ibrahim Aljarah

引用次数: 6

Entity-relationship queries over wikipedia 维基百科上的实体关系查询

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1871991

Xiaonan Li, Chengkai Li, Cong Yu

引用次数: 23

Mining social tags to predict mashup patterns 挖掘社会标签来预测混搭模式

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1871998

Khaled Goarany, Gregory Kulczycki, M. Brian Blake

引用次数: 26

Cross-media impact on twitter in japan 日本twitter的跨媒体影响

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1872003

Sayaka Akioka, N. Kato, Y. Muraoka, H. Yamana

{"title":"Cross-media impact on twitter in japan","authors":"Sayaka Akioka, N. Kato, Y. Muraoka, H. Yamana","doi":"10.1145/1871985.1872003","DOIUrl":"https://doi.org/10.1145/1871985.1872003","url":null,"abstract":"Twitter, a microblogging service, is now grabbing attention of people as a new channel. For deep understanding of this new service, this paper reports the characteristics of Twitter users in Japan, and the impact of media such as publications, and TV programs on Twitter community. To the best of our knowledge, this paper is the first to analyze mutual impact between Twitter, and other media quantitatively.\u0000 In order for the analyses, we crawled user profiles whose language setting is Japanese, and conducted several analysis with well-known methodologies as conventional work did. We confirmed the characteristics of the collected user profiles. We observed the distributions of the number of friends, and the number of follows both follow power-law, and there exists the correlation between the number of friends, and the number of follows.\u0000 Besides the collected user profiles, we also utilized closed caption data of TV programs in Japan, and other information on media picked up Twitter. We run a batch of matching these data outside Twitter with the collected user profiles, and concluded Twitter has been already widely spread among Japanese people, however, media have still huge impact on the growth of Twitter users. We also conjectured the impact is not one-sided, however, is mutual influence between Twitter, and other media.","PeriodicalId":244822,"journal":{"name":"SMUC '10","volume":"482 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116807196","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: 11

Spam detection with a content-based random-walk algorithm 基于内容的随机漫步算法的垃圾邮件检测

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1871994

F. Javier Ortega, C. Macdonald, J. A. Troyano, Fermín L. Cruz

引用次数: 9

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