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Topic Classification for Short Texts

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Advances in Information Systems Development (ISD 2022)

Part of the book series: Lecture Notes in Information Systems and Organisation ((LNISO,volume 63))

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Abstract

In the context of TV and social media surveillance, constructing models to automate topic identification of short texts is a key task. This paper constructs worth-to-consider models for practical usage, employing Top-K multinomial classification methodology. We describe the full data processing pipeline, discussing about dataset selection, text preprocessing, feature extraction, model selection and learning, including hyperparameter optimization. We will test and compare popular methods including: standard machine learning, deep learning, and a fine-tuned BERT for topic classification.

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Notes

  1. 1.

    https://jmcauley.ucsd.edu/data/amazon/.

  2. 2.

    http://trec.nist.gov/data/tweets.

  3. 3.

    https://www.kaggle.com/datasets/rmisra/news-category-dataset.

  4. 4.

    https://www.huffpost.com/, formerly The Huffington Post until 2017, is an American news aggregator and blog with localized and international editions.

  5. 5.

    https://spacy.io/.

  6. 6.

    https://radimrehurek.com/gensim/.

  7. 7.

    https://huggingface.co/docs/transformers/main_classes/tokenizer.

  8. 8.

    https://huggingface.co/docs/transformers/model_doc/bert.

  9. 9.

    https://sklearn-genetic-opt.readthedocs.io/.

  10. 10.

    https://github.com/deap/deap.

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Acknowledgements

This paper was financed by the project with the title “Platformă inovativă pentru măsurarea audienţei TV, identificarea automată a telespectatorilor şi corelarea cu date analitice din platforme de socializare online” (Innovative platform for measuring TV audience, automatic identification of viewers and correlating it with analytic data from social media). The project was cofinanced by “Fondul European de Dezvoltare Regională prin Programul Operaţional Competitivitate (POC) 2014–2020, Axa prioritară: 2-Tehnologia Informaţiei şi Comunicaţiilor (TIC) pentru o economie digitală competitivă”. (the European Regional Development Fund (ERDF) through the Competitiveness Operational Program 2014–2020, Priority Axis 2 - Information and Communication Technology (ICT) for a competitive digital economy), project code SMIS 2014+:128960, beneficiary: CICADA TECHNOLOGIES S.R.L. The project is part of the call: POC/524/2/2/ “Sprijinirea creşterii valorii adăugate generate de sectorul TIC şi a inovării în domeniu prin dezvoltarea de clustere” (Supporting the added value generated by the ICT sector and innovation in the field through cluster development). The content of this material does not necessarily represent the official position of the European Union or the Romanian Government.

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Authors and Affiliations

  1. Cicada Technologies, Bd. Nicolae Titulescu 18 apt. 82, 400420, Cluj-Napoca, Romania

    Dan Claudiu Neagu, Andrei Bogdan Rus, Mihai Grec & Mihai Boroianu

  2. Babes-Bolyai University, Str. Theodor Mihali 58-60, 400591, Cluj-Napoca, Romania

    Dan Claudiu Neagu & Gheorghe Cosmin Silaghi

  3. Technical University, Str. Memorandumului 28, 400114, Cluj-Napoca, Romania

    Andrei Bogdan Rus

Authors
  1. Dan Claudiu Neagu
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  4. Mihai Boroianu
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  5. Gheorghe Cosmin Silaghi
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Correspondence to Gheorghe Cosmin Silaghi .

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Editors and Affiliations

  1. Business Informatics Research Center, Babes-Bolyai University, Cluj-Napoca, Romania

    Gheorghe Cosmin Silaghi

  2. Business Informatics Research Center, Babes-Bolyai University, Cluj Napoca, Romania

    Robert Andrei Buchmann

  3. Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania

    Virginia Niculescu

  4. Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania

    Gabriela Czibula

  5. Business Information Systems Discipline, University of Galway, Galway, Ireland

    Chris Barry

  6. Business Information Systems Discipline, University of Galway, Galway, Ireland

    Michael Lang

  7. Department of Human-Centred Computing, Faculty of Information Technology, Monash University, Clayton, VIC, Australia

    Henry Linger

  8. IESE Business School, University of Navarra, Barcelona, Spain

    Christoph Schneider

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Neagu, D.C., Rus, A.B., Grec, M., Boroianu, M., Silaghi, G.C. (2023). Topic Classification for Short Texts. In: Silaghi, G.C., et al. Advances in Information Systems Development. ISD 2022. Lecture Notes in Information Systems and Organisation, vol 63. Springer, Cham. https://doi.org/10.1007/978-3-031-32418-5_12

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