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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 PREDICTING MUSIC GENRE PREFERENCES BASED ON ONLINE COMMENTS Sinclair, Andrew J In: Master's Theses (2014) Abstract: Communication Accommodation Theory (CAT) states that individuals adapt to each other’s communicative behaviors. This adaptation is called “convergence.” In this work we explore the convergence of writing styles of users of the online music distribution plat- form SoundCloud.com. In order to evaluate our system we created a corpus of over 38,000 comments retrieved from SoundCloud in April 2014. The corpus represents comments from 8 distinct musical genres: Classical, Electronic, Hip Hop, Jazz, Country, Metal, Folk, and World. Our corpus contains: short comments, frequent misspellings, little sentence struc- ture, hashtags, emoticons, and URLs. We adapt techniques used by researchers analyzing other short web-text corpora in order to deal with these problems. We use a supervised machine learning approach to classify the genre of comments in our corpus. We examine the effects of different feature sets and supervised machine learning algorithms on classification accuracy. In total we ran 180 experiments in which we varied: number of genres, feature set composition, and machine learning algorithm. In experiments with all 8 genres we achieve up to 40% accuracy using either a Naive Bayes classifier or C4.5 based classifier with a feature set consisting of 1262 token unigrams and bigrams. This represents a 3 time improvement over chance levels. Keyword: Artificial Intelligence and Robotics; Computational Linguistics; Corpus Linguistics; Machine Learning; Natural Language Processing URL: https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=2333&context=theses https://digitalcommons.calpoly.edu/theses/1268 BASE Hide details 2 Entity Information Extraction using Structured and Semi-structured resources ... Unkn Unknown. - : Temple University. Libraries, 2014 BASE Show details 3 The USAGE review corpus for fine-grained, multi-lingual opinion analysis ... Klinger, Roman. - : Bielefeld University, 2014 BASE Show details 4 The USAGE review corpus for fine-grained, multi-lingual opinion analysis ... Klinger, Roman. - : Bielefeld University, 2014 BASE Show details 5 The USAGE review corpus for fine-grained, multi-lingual opinion analysis Klinger, Roman. - : Bielefeld University, 2014 BASE Show details 6 Deep stochastic sentence generation : resources and strategies Mille, Simon. - : Universitat Pompeu Fabra, 2014 In: TDX (Tesis Doctorals en Xarxa) (2014) BASE Show details 7 Supervised and semi-supervised statistical models for word-based sentiment analysis ; Überwachte und halbüberwachte statistische Modelle zur wortbasierten Sentimentanalyse Scheible, Christian. - 2014 BASE Show details 8 Identification of Informativeness in Text using Natural Language Stylometry Shams, Rushdi In: Electronic Thesis and Dissertation Repository (2014) BASE Show details

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