A zero-shot classification pipeline that uses Natural Language Inference for classification of 1,000 tweets into a two-layer taxonomy of 30 parent topics and 330 subtopics has been created without any human-labeled samples.
Abstract
Text sentiment analysis of the social media text faces challenges posed by unstructured data and labori- ous human labeling for intent-driven, hierarchical classification. This work compares conventional ML models (SVM, Naïve Bayes, Logistic Regression) with contextual DL models (DistilBERT) in terms of their performance on Sentiment140 dataset (1.6 million tweets) where a balanced 300,000 tweets were selected (200,000 training, 50,000 validation and 50,000 test). As a solution to the bottleneck of human labeling for detailed topic classification, a zero-shot classification pipeline that uses Natural Language Inference (NLI) for classification of 1,000 tweets into a two-layer taxonomy of 30 parent topics and 330 subtopics has been created without any human-labeled samples. SVM is able to achieve 81.52% accuracy, while DistilBERT scores 84.44% on 50,000 tweet test set and 83.0% on a small 1,000 tweets sample.
Social media provides a large volume of informal textual data for examining public sentiment, but Indonesian Twitter content presents substantial challenges due to slang, nonstandard spelling, limited context, and uneven class distribution. This study evaluates a reproducible machine-learning and natural-language-proce...
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Sentiment analysis is an important Natural Language Processing (NLP) task used to automatically identify the emotional orientation expressed in textual data. Although recent research increasingly focuses on deep learning and transformer-based language models, classical machine-learning methods combined with Term Freque...
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Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
