SMUC '10最新文献_第2页

On the difficulty of clustering company tweets 关于公司推文聚类的难度

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1872001

Fernando Pérez-Téllez, David Pinto, J. Cardiff, Paolo Rosso

{"title":"On the difficulty of clustering company tweets","authors":"Fernando Pérez-Téllez, David Pinto, J. Cardiff, Paolo Rosso","doi":"10.1145/1871985.1872001","DOIUrl":"https://doi.org/10.1145/1871985.1872001","url":null,"abstract":"Twitter is a new successful technology of the Web 2.0 genre which is used by millions of people and companies to publish brief messages (\"tweets\") with the purpose of sharing experiences and/or opinions about a product or service. Due to the huge amount of information available in this type of technology, there is a clear need for new systems that can mine these messages in order to derive information about the collective thinking of twitterers (e.g. for opinion or sentiment analysis). Tweet analysis is a very important task because comments, opinions, suggestions, complaints can be used as marketing strategies or for determining information on a company's reputation. For this purpose, it is necessary to establish whether a tweet refers to a company or not, which is not a straightforward keyword search process as there may be multiple contexts in which a name can be used. The aim of this work is to present and compare a number of different approaches based on clustering that determine whether a given tweet refers to a particular company or not. For this purpose, we have used an enriching methodology in order to improve the representation of tweets and as a consequence the performance of the clustering company tweets task. The obtained results are promising and highlight the difficulty of this task.","PeriodicalId":244822,"journal":{"name":"SMUC '10","volume":"15 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":"134456631","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: 39

A knowledge-rich approach to feature-based opinion extraction from product reviews 从产品评论中提取基于特征的意见的知识丰富的方法

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1871990

Fermín L. Cruz, Javier Ortega

引用次数: 33

Exploiting tag and word correlations for improved webpage clustering 利用标签和词的相关性来改进网页聚类

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1871989

Anusua Trivedi, Piyush Rai, S. Duvall, Hal Daumé

{"title":"Exploiting tag and word correlations for improved webpage clustering","authors":"Anusua Trivedi, Piyush Rai, S. Duvall, Hal Daumé","doi":"10.1145/1871985.1871989","DOIUrl":"https://doi.org/10.1145/1871985.1871989","url":null,"abstract":"Automatic clustering of webpages helps a number of information retrieval tasks, such as improving user interfaces, collection clustering, introducing diversity in search results, etc. Typically, webpage clustering algorithms only use features extracted from the page-text. However, the advent of social-bookmarking websites, such as StumbleUpon and Delicious, has led to a huge amount of user-generated content such as the tag information that is associated with the webpages. In this paper, we present a subspace based feature extraction approach which leverages tag information to complement the page-contents of a webpage to extract highly discriminative features, with the goal of improved clustering performance. In our approach, we consider page-text and tags as two separate views of the data, and learn a shared subspace that maximizes the correlation between the two views. Any clustering algorithm can then be applied in this subspace. We compare our subspace based approach with a number of baselines that use tag information in various other ways, and show that the subspace based approach leads to improved performance on the webpage clustering task. Although our results here are on the webpage clustering task, the same approach can be used for webpage classification as well. In the end, we also suggest possible future work for leveraging tag information in webpage clustering, especially when tag information is present for not all, but only for a small number of webpages.","PeriodicalId":244822,"journal":{"name":"SMUC '10","volume":"84 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":"128005185","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: 16

Exploiting web reviews for generating customer service surveys 利用网络评论生成客户服务调查

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1871995

Suke Li, Zhong Chen

引用次数: 3

Smuc 2010 industry panel Smuc 2010行业研讨会

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1872006

N. Wanas, M. Makrehchi, José Carlos Cortizo

引用次数: 0

Web-based statistical fact checking of textual documents 基于web的文本文档统计事实检查

SMUC '10 Pub Date : 2010-10-30 DOI: 10.1145/1871985.1872002

A. Magdy, N. Wanas