Web users while collaborating over social networks and micro-blogging services also contribute to news coverage worldwide. News feeds come from mainstream media as well as from social networks. Often feeds from social networks are more up-to-date and, for user's view, more credible than those that come from mainstream media. But the overwhelming amount of information requires to personally filter through it until one gets what is really needed. In this paper, we describe our idea of a personalized news network built on current Web technologies and our research projects by filtering Twitter and Facebook messages using both trend mining and reputation approaches. Based on the example of Egyptian revolution, we explain the main idea of personalized news.
{"title":"Trend-based and reputation-versed personalized news network","authors":"Olga Streibel, R. Alnemr","doi":"10.1145/2065023.2065027","DOIUrl":"https://doi.org/10.1145/2065023.2065027","url":null,"abstract":"Web users while collaborating over social networks and micro-blogging services also contribute to news coverage worldwide. News feeds come from mainstream media as well as from social networks. Often feeds from social networks are more up-to-date and, for user's view, more credible than those that come from mainstream media. But the overwhelming amount of information requires to personally filter through it until one gets what is really needed. In this paper, we describe our idea of a personalized news network built on current Web technologies and our research projects by filtering Twitter and Facebook messages using both trend mining and reputation approaches. Based on the example of Egyptian revolution, we explain the main idea of personalized news.","PeriodicalId":341071,"journal":{"name":"SMUC '11","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128229361","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}
This talk is focused on a key task in the area of Opinion Mining and Sentiment Analysis: polarity classification of social media documents (e.g. blog posts). Estimating polarity is much more demanding than estimating topicality. As a matter of fact, the effectiveness of polarity classification is still modest and does not compare with the effectiveness of standard retrieval tasks. Polarity estimation is severely affected by parts of the text that are off-topic or that simply do not express any opinion. In fact, the key sentiments in a document often appear in specific locations of the text. Furthermore, there are usually conflicting opinions in a given document and this mixed set of opinions harms the performance of automatic methods designed to estimate the overall orientation of the text. In this talk, I will argue that understanding the flow of sentiments in a text is a major challenge for effectively predicting the document's orientation towards a given topic. I will briefly outline some possible avenues to address this challenging issue and review some recent papers that take steps in this direction.
{"title":"The challenge of understanding the flow of sentiments in social media documents","authors":"D. Losada","doi":"10.1145/2065023.2065025","DOIUrl":"https://doi.org/10.1145/2065023.2065025","url":null,"abstract":"This talk is focused on a key task in the area of Opinion Mining and Sentiment Analysis: polarity classification of social media documents (e.g. blog posts). Estimating polarity is much more demanding than estimating topicality. As a matter of fact, the effectiveness of polarity classification is still modest and does not compare with the effectiveness of standard retrieval tasks. Polarity estimation is severely affected by parts of the text that are off-topic or that simply do not express any opinion. In fact, the key sentiments in a document often appear in specific locations of the text. Furthermore, there are usually conflicting opinions in a given document and this mixed set of opinions harms the performance of automatic methods designed to estimate the overall orientation of the text.\u0000 In this talk, I will argue that understanding the flow of sentiments in a text is a major challenge for effectively predicting the document's orientation towards a given topic. I will briefly outline some possible avenues to address this challenging issue and review some recent papers that take steps in this direction.","PeriodicalId":341071,"journal":{"name":"SMUC '11","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132486872","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}
Folksonomies are becoming increasingly popular, both among users who find them simple and intuitive to use, and scientists as interesting research objects. Folksonomies can be viewed as large informal sources of semantics. Harnessing the semantics for search or concept extraction requires us to be able to recognize linguistic similarity between tags. In this paper we propose an approach that uses a combination of morpho-syntactic and semantic similarity measures without using any external linguistic resources to mine tag pairs that can be reduced to base tags. Our approach is based on the Levenshtein distance for morpho-syntactic similarity and tag signatures for semantic similarity. The evaluation of our approach, based on a data set crawled from Delicious, shows that we are able to recognize a wide range of linguistic variations with high quality.
{"title":"Mining tag similarity in folksonomies","authors":"Geir Solskinnsbakk, J. Gulla","doi":"10.1145/2065023.2065037","DOIUrl":"https://doi.org/10.1145/2065023.2065037","url":null,"abstract":"Folksonomies are becoming increasingly popular, both among users who find them simple and intuitive to use, and scientists as interesting research objects. Folksonomies can be viewed as large informal sources of semantics. Harnessing the semantics for search or concept extraction requires us to be able to recognize linguistic similarity between tags. In this paper we propose an approach that uses a combination of morpho-syntactic and semantic similarity measures without using any external linguistic resources to mine tag pairs that can be reduced to base tags. Our approach is based on the Levenshtein distance for morpho-syntactic similarity and tag signatures for semantic similarity. The evaluation of our approach, based on a data set crawled from Delicious, shows that we are able to recognize a wide range of linguistic variations with high quality.","PeriodicalId":341071,"journal":{"name":"SMUC '11","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130701294","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}
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