| CODE | LIN3506 | ||||||
| TITLE | Text Analysis - Psychological, Affective and Forensic Applications | ||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||
| MQF LEVEL | 6 | ||||||
| ECTS CREDITS | 4 | ||||||
| DEPARTMENT | Institute of Linguistics and Language Technology | ||||||
| DESCRIPTION | This unit will focus on techniques involving the extraction from text of information pertaining to its author's subjective or "private" states, as well as their relevance to various applications. The topics covered are the following: - mining text for personal opinions on a specific subject (opinion mining); - identifying positive or negative affect in text (sentiment analysis); - identifying and tracking multiple points of view in narrative; - finding evidence in text for an author's personality traits. All of the above topics have become the subjects of intensive research in the last decade. Most of this research involves the use of machine learning techniques, whereby the problem is framed as a classification or extraction problem. Hence, this course will introduce students to a variety of such techniques, and will encourage them to put them to use during practical sessions. Students will also be introduced to problems in annotating text for subjectivity mining, as well as techniques for computing inter-annotator reliability. In addition, the unit also covers the broader applicability of mining text for subjectivity, especially in fields such as web technology and business intelligence. Study-unit Aims This unit will give students a grounding in those aspects of text and discourse which shed light on some personal or subjective dimensions related to its producer (author or speaker). It also introduces students to state of the art techniques for identifying and extracting such subtle information. Learning Outcomes 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - distinguish between various subjective or personal dimensions that can be identified in discourse; - apply machine-learning techniques to mine text; - evaluate the outcomes of automatic extraction using human or automated means. 2. Skills: By the end of the study-unit the student will be able to: - appreciate the importance of discourse in shedding light on a person's personal or subjective stance on different issues; - apply their practical knowledge of automatic methods for mining text to solve new problems. Main Text/s and any supplementary readings Students taking this unit will be provided with a reading pack with articles, including the following: - C. Alm, D. R. Ovesdotter & Richard Sproat (2005). Emotions from text: Machine learning for text-based emotion prediction. In Proceedings of the Human Language Technology Conference and the 2005 Conference on Empirical Methods in Natural Language Processing, Vancouver, B.C., Canada, 6–8 October 2005, pp. 579–586. [Unavailable in UoM Library; freely available from ACL Anthology http://aclweb.org/anthology-new/] - C.H. Chung and J. W. Pennebaker (2007). The psychological function of function words. In K. Fiedler (Ed.), Social communication: Frontiers of social psychology (pp 343-359). New York: Psychology Press. [Unavailable in UoM Library] - A. Esuli & F. Sebastiani (2006a). Determining term subjectivity and term orientation for opinion mining. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, Italy, 3–7 April 2006, pp. 193–200. [Unavailable in UoM Library; freely available from ACL Anthology http://aclweb.org/anthology-new/] - V. Hatzivassiloglou & K. R.McKeown (1997). Predicting the semantic orientation of adjectives. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and of the 8th Conference of the European Chapter of the Association for Computational Linguistics, Madrid, Spain, 7–12 July 1997, pp. 174–181. [Unavailable in UoM Library; freely available from ACL Anthology http://aclweb.org/anthology-new/] - D. Jurafsky & J. H. Martin (2009). Speech and Language Processing. (2nd edition). Indiana: Prentice Hall [Unavailable in UoM Library] - M. Laver, K. Benoit & J. Garry (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97(2):311–331. [Unavailable in UoM Library] - R. Mihalcea & C. Strapparava (2006). Learning to laugh (automatically): Computational models for humor recognition. Computational Intelligence, 22(2):126–142. [Unavailable in UoM Library] - T. Mitchell (1998). Machine learning. McGraw Hill [Available in UoM Library] - J. M. Wiebe (1994). Tracking point of view in narrative. Computational Linguistics, 20(2):233–287. [Unavailable in UoM Library; freely available from ACL Anthology http://aclweb.org/anthology-new/] |
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| ADDITIONAL NOTES | Pre-Requisite Study-units LIN3098 Corpus Linguistics, CSA5011 Corpora and Statistical Methods in NLP |
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| STUDY-UNIT TYPE | Lecture and Practicum | ||||||
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
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