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The WebGenre Blog: The power of genre applied to digital information. By Marina Santini » Entries tagged with "sentiment analysis"

Lecture: Sentiment Analysis

Lecture: Sentiment Analysis

Topics: sentiment analysis, affective meaning, connotational aspects, sentiment lexicons, naive bayes baseline algorithm, mutual information, pointwise mutual information, computational semantics, likelihood, Scherer’s typology, emotion classification, opinion mining, sentiment mining, subjectivity analysis, manually-built sentiment lexicons, semi-supervised methods, SentiWordnet, General Enquirer, earning sentiment lexicons, Turney algorithm Sentiment Analysis from Marina Santini … Read entire article »

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Course Start: Semantic Analysis in Language Technology

Spring 2016 Semantic Analysis in Language Technology at Uppsala University (Sweden) http://stp.lingfil.uu.se/~santinim/sais/2016/sais_2016.htm Topics: Semantics and Computational Semantics Semantic Role Labelling/Predicate-Argument Structure Sentiment Analysis Word Sense Disambiguation Vector Semantics Information Extraction (I & II) Question Answering (I & II) Ontologies and the Semantic Web (I & II) … Read entire article »

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Lecture 3: Structuring the Unstructured via Sentiment Analysis

Lecture 3: Structuring Unstructured Texts Through Sentiment Analysis from Marina Santini … Read entire article »

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Course: Semantic Analysis in Language Technology

Uppsala University: Department of Linguistics and Philology Semantic Analysis in Language Technology (2013)         Credits: 7,5 hp Syllabus: 5LN456 Teacher: Marina Santini The course website will be update regularly during the teaching session with additional material. Last Updated: 23 October 2013 Course website: http://stp.lingfil.uu.se/~santinim/sais/sais_fall2013.htm Nov, 12 (Tue) 10‑12 9-2042 (Turing) Course introduction [OH]. J&M 17–18 Nov, 14 (Thu) 10-12 9-2042 (Turing) Introduction to essay assignment (EA) [OH]. Nov, 19 (Tue) 10-12 9-2042 (Turing) IE/PAS, PAS assignment [OH] Johansson and Nugues 2008, J&M 20.9 Nov, 21 (Thu) 10-12 9-2042 (Turing) EA and PAS supervision – Nov, 26 (Tue) 10-12 9-2042 (Turing) Sentiment analysis BL 1–4 Nov, 28 (Thu) 10-12 9-2042 (Turing) Sentiment analysis BL 5–7 Dec, 03 (Tue) 10-12 9-2042 (Turing) Supervision – Dec, 06 (Thu) Deadline EA, step 1 Dec, 10 (Tue) 10-12 9-2042 (Turing) EA presentations – Dec, 12 (Thu) 10-12 9-2042 (Turing) WSD [OH] J&M 19–20. Dec, 17 (Tue) 10-12 9-2042 (Turing) WSD. Deadline EA, feedback to another group (link to submitted essays below) – Jan, 20 (Mon) 2014-01-20: Deadline, all assignments Intended learning outcomes In order to pass the course, a student must be able to: describe systems that perform the following tasks, apply them to authentic linguistic data, and evaluate the results: disambiguate instances of polysemous lemmas [word sense disambiguation, WSD]; use semantic analysis in the context of information extraction … Read entire article »

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Lecture 7: Learning from Massive Datasets

Lecture 7: Learning from Massive Datasets from Marina Santini In this lecture we explore how big datasets can be used with the Weka workbench and what other issues are currently under discussion in the real world, for ex: big data applications, predictive linguistic analysis, new platforms and new programming languages. … Read entire article »

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Presentation: How Emotional Are Users’ Needs? Emotion in Query Logs

According to recent IR research, searchers’ behaviour is not only limited to traditional informational, navigational and transactional needs. A novel hypothesis is that the seeking behaviour is driven by emotion. These experiments are part of SearchInFocus, a study centred on search. How Emotional Are Users’ Needs? Emotion in Query Logs from Marina Santini http://www.cyberemotions.eu/ … Read entire article »

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Reading Suggestions: Genre & Sentiment-Emotion

Lex E., Juffinger A. and Granitzer M. (2010). A Comparison of Stylometric and Lexical Features for Web Genre Classification and Emotion Classification in Blogs. DEXA Workshops 2010. Abstract: In the blogosphere, the amount of digital content is expanding and for search engines, new challenges have beenimposed. Due to the changing information need, automatic methods are needed to support blog search users to filter informationby different facets. … Read entire article »

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