Abstract
The Author Profiling (AP) task aims to determine specific demographic characteristics such as gender and age, by analyzing the language usage in groups of authors. Notwithstanding the recent advances in AP, this is still an unsolved problem, especially in the case of social media domains. According to the literature most of the work has been devoted to the analysis of useful textual features. The most prominent ones are those related with content and style. In spite of the success of using jointly both kinds of features, most of the authors agree in that content features are much more relevant than style, which suggest that some profiling aspects, like age or gender could be determined only by observing the thematic interests, concerns, moods, or others words related to events of daily life. Additionally, most of the research only uses traditional representations such as the BoW, rather than other more sophisticated representations to harness the content features. In this regard, this paper aims at evaluating the usefulness of some topic-based representations for the AP task. We mainly consider a representation based on Latent Semantic Analysis (LSA), which automatically discovers the topics from a given document collection, and a simplified version of the Linguistic Inquiry and Word Count (LIWC), which consists of 41 features representing manually predefined thematic categories. We report promising results in several corpora showing the effectiveness of the evaluated topic-based representations for AP in social media.
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Notes
- 1.
It is very hard to accurately apply typical procedures like stemming or extract specific syntactic information from informal documents.
- 2.
In AP tasks, several authors have used LSA as part of elaborated strategies involving different kinds of features, for example: ensemble strategies, or fusion strategies [21]. Nevertheless, they have not reported experimental results to show the real contribution of LSA features.
References
Argamon, S., Koppel, M., Pennebaker, J.W., Schler, J.: Mining the blogosphere: age, gender and the varieties of self-expression. First Monday 12(9) (2007)
Argamon, S., Koppel, M., Pennebaker, J.W., Schler, J.: Automatically profiling the author of an anonymous text. Commun. ACM 52(2), 119–123 (2009)
Bergsma, S., Post, M., Yarowsky, D.: Stylometric analysis of scientific articles. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 327–337. Association for Computational Linguistics (2012)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391 (1990)
Eckert, P.: Age as a sociolinguistic variable. In: The Handbook of Sociolinguistics, pp. 151–167 (1997)
Evangelopoulos, N.E.: Latent semantic analysis. Wiley Interdiscip. Rev.: Cogn. Sci. 4(6), 683–692 (2013)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Fink, C., Kopecky, J., Morawski, M.: Inferring gender from the content of tweets: a region specific example. In: ICWSM (2012)
Garera, N., Yarowsky, D.: Modeling latent biographic attributes in conversational genres. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 2, pp. 710–718. Association for Computational Linguistics (2009)
Goswami, S., Sarkar, S., Rustagi, M.: Stylometric analysis of bloggers age and gender. In: Third International AAAI Conference on Weblogs and Social Media (2009)
Holmes, J., Meyerhoff, M.: The Handbook of Language and Gender, vol. 25. Wiley, Hoboken (2008)
Iqbal, H.R., Ashraf, M.A., Nawab, R.M.A.: Predicting an author’s demographics from text using topic modeling approach (2015)
Kahn, J.H., Tobin, R.M., Massey, A.E., Anderson, J.A.: Measuring emotional expression with the linguistic inquiry and word count. Am. J. Psychol. 263–286 (2007)
Koppel, M., Argamon, S., Shimoni, A.R.: Automatically categorizing written texts by author gender. Lit. Linguist. Comput. 17(4), 401–412 (2002)
Landauer, T.K., Dumais, S.T.: A solution to plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol. Rev. 104(2), 211 (1997)
Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse processes 25(2–3), 259–284 (1998)
Landauer, T.K., McNamara, D.S., Dennis, S., Kintsch, W.: Handbook of Latent Semantic Analysis. Psychology Press, Abingdon (2013)
López-Monroy, A.P., Montes-y-Gómez, M., Escalante, H.J., Villaseñor-Pineda, L.: Using intra-profile information for author profiling. In: CLEF (Working Notes) (2014)
López-Monroy, A.P., y Gómez, M.M., Escalante, H.J., Villaseñor-Pineda, L., Stamatatos, E.: Discriminative subprofile-specific representations for author profiling in social media. Knowl.-Based Syst. 89, 134–147 (2015)
McCollister, C., Huang, S., Luo, B.: Building topic models to predict author attributes from twitter messages (2015)
Meina, M., Brodzinska, K., Celmer, B., Czokow, M., Patera, M., Pezacki, J., Wilk, M.: Ensemble-based classification for author profiling using various features notebook for PAN at CLEF 2013. In: CLEF (Working Notes) (2013)
Newman, M.L., Groom, C.J., Handelman, L.D., Pennebaker, J.W.: Gender differences in language use: an analysis of 14,000 text samples. Discourse Process. 45(3), 211–236 (2008)
Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: How old do you think i am?: A study of language and age in twitter. In: Seventh International AAAI Conference on Weblogs and Social Media (2013)
Nguyen, D., Smith, N.A., Rosé, C.P.: Author age prediction from text using linear regression. In: Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, pp. 115–123. Association for Computational Linguistics (2011)
Pennacchiotti, M., Popescu, A.M.: Democrats, republicans and starbucks afficionados: user classification in twitter. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 430–438. ACM (2011). http://doi.acm.org/10.1145/2020408.2020477
Pennebaker, J.W., Stone, L.D.: Words of wisdom: language use over the life span. J. Personal. Soc. Psychol. 85(2), 291 (2003)
Rangel, F., Rosso, P., Chugur, I., Potthast, M., Trenkmann, M., Stein, B., Verhoeven, B., Daelemans, W.: Overview of the author profiling task at PAN 2014. In: CLEF (Online Working Notes/Labs/Workshop), pp. 898–927 (2014)
Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN 2013. In: Notebook Papers of CLEF 2013 LABs and Workshops, CLEF-2013, Valencia, Spain, September, pp. 23–26 (2013)
Rude, S., Gortner, E.M., Pennebaker, J.: Language use of depressed and depression-vulnerable college students. Cogn. Emot. 18(8), 1121–1133 (2004)
Sarawgi, R., Gajulapalli, K., Choi, Y.: Gender attribution: tracing stylometric evidence beyond topic and genre. In: Proceedings of the Fifteenth Conference on Computational Natural Language Learning, pp. 78–86. Association for Computational Linguistics (2011)
Schler, J., Koppel, M., Argamon, S., Pennebaker, J.: Effects of age and gender on blogging. In: Proceedings of 2006 AAAI Spring Symposium on Computational Approaches for Analyzing Weblogs, pp. 199–205 (2006)
Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Ramones, S.M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M.E., et al.: Personality, gender, and age in the language of social media: the open-vocabulary approach. PloS One 8(9), e73791 (2013)
Schwartz, H.A., Eichstaedt, J.C., Dziurzynski, L., Kern, M.L., Blanco, E., Kosinski, M., Stillwell, D., Seligman, M.E., Ungar, L.H.: Toward personality insights from language exploration in social media. In: AAAI Spring Symposium: Analyzing Microtext (2013)
Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)
Turney, P.: Mining the web for synonyms: Pmi-ir versus lsa on toefl (2001)
Weren, E.R., Kauer, A.U., Mizusaki, L., Moreira, V.P., de Oliveira, J.P.M., Wives, L.K.: Examining multiple features for author profiling. J. Inf. Data Manag. 5(3), 266 (2014)
Wiemer-Hastings, P., Wiemer-Hastings, K., Graesser, A.: Latent semantic analysis. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, pp. 1–14. Citeseer (2004)
Acknowledgments
This work was partially supported by CONACYT under scholarships 401887 and 243957, project 247870, and the Thematic Network in Language Technologies, projects 260178 and 271622.
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Álvarez-Carmona, M.A., López-Monroy, A.P., Montes-y-Gómez, M., Villaseñor-Pineda, L., Meza, I. (2016). Evaluating Topic-Based Representations for Author Profiling in Social Media. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_13
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