Topic and Sentiment Classification

March 22, 2023 - 2 minute read - Category: Intro - Tags: Deep learning


This post covers the fourteenth lecture in the course: “Topic and Sentiment Classification.”

Classifying Text is key to several social science applications. This lecture covers two key applications of text classification: topic and sentiment classification.

Lecture Video

Lecture notes

References Cited in Lecture 14: Topic and Sentiment Classification

Yin, Wenpeng, Jamaal Hay, and Dan Roth. “Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach.” arXiv preprint arXiv:1909.00161 (2019)., 2020.

Schick, Timo, and Hinrich Schütze. “Exploiting cloze questions for few-shot text classification and natural language inference.” arXiv preprint arXiv:2001.07676 (2020).

Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. ““Why should i trust you?” Explaining the predictions of any classifier.” In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135-1144. 2016.

Munikar, Manish, Sushil Shakya, and Aakash Shrestha. “Fine-grained sentiment classification using BERT.” In 2019 Artificial Intelligence for Transforming Business and Society (AITB), vol. 1, pp. 1-5. IEEE, 2019.

Hewitt, John, and Christopher D. Manning. “A structural probe for finding syntax in word representations.” In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4129-4138. 2019.

Han, Jiyoung, Youngin Lee, Junbum Lee, and Meeyoung Cha. “The fallacy of echo chambers: Analyzing the political slants of user-generated news comments in Korean media.” In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pp. 370-374. 2019.

Schiller, Benjamin, Johannes Daxenberger, and Iryna Gurevych. “Stance detection benchmark: How robust is your stance detection?” KI-Künstliche Intelligenz 35, no. 3 (2021): 329-341.

Glandt, Kyle, Sarthak Khanal, Yingjie Li, Doina Caragea, and Cornelia Caragea. “Stance detection in COVID-19 tweets.” In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1596-1611. 2021.

Grootendorst, Maarten. “BERTopic: Neural topic modeling with a class-based TF-IDF procedure.” arXiv preprint arXiv:2203.05794 (2022).

Lin, Yuxiao, Yuxian Meng, Xiaofei Sun, Qinghong Han, Kun Kuang, Jiwei Li, and Fei Wu. “Bertgcn: Transductive text classification by combining gcn and bert.” arXiv preprint arXiv:2105.05727 (2021).

Other Resources

The Illustrated Bert:

Joe Davison, Zero Shot Learning in Modern NLP”, 2020.

Huggingface. “New Pipeline for zero-shot text classification” , 2020.


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