Search results for: text-mining

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text-mining</title> <meta name="description" content="Search results for: text-mining"> <meta name="keywords" content="text-mining"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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style="font-size:.9rem"><span class="badge badge-info">1</span> “Octopub”: Geographical Sentiment Analysis Using Named Entity Recognition from Social Networks for Geo-Targeted Billboard Advertising</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oussama%20Hafferssas">Oussama Hafferssas</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiba%20Benyahia"> Hiba Benyahia</a>, <a href="https://publications.waset.org/abstracts/search?q=Amina%20Madani"> Amina Madani</a>, <a href="https://publications.waset.org/abstracts/search?q=Nassima%20Zeriri"> Nassima Zeriri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=textmining" title="textmining">textmining</a>, <a href="https://publications.waset.org/abstracts/search?q=named%20entity%20recognition%28NER%29" title=" named entity recognition(NER)"> named entity recognition(NER)</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media%20networks%20%28SN" title=" social media networks (SN"> social media networks (SN</a>, <a href="https://publications.waset.org/abstracts/search?q=SMN%29" title="SMN)">SMN)</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence%28BI%29" title=" business intelligence(BI)"> business intelligence(BI)</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing" title=" marketing"> marketing</a> </p> <a href="https://publications.waset.org/abstracts/15721/octopub-geographical-sentiment-analysis-using-named-entity-recognition-from-social-networks-for-geo-targeted-billboard-advertising" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15721.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">590</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> 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Search results for: text-mining