Performance Evaluation of Text Embedding Models for Ambiguity Classification in Indonesian News Corpus: A Comparative Study of TF-IDF, Word2Vec, FastText BERT, and GPT
Comparisons of the performance of five text embedding models TF-IDF, Word2Vec, FastText, BERT, and GPT combined with five binary classification algorithms highlight the critical role of selecting appropriate embedding and classification models to enhance accuracy in semantically ambiguous sentence classification for the Indonesian language.
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...
Aan Ansori, Raden Kania, Tifani Intan Solihati et al.· Transactions on Informatics...· 0 citations
Overall, this work demonstrates that incorporating explicit linguistic information, including language identity, sentiment polarity, and intensifier information, improves sentiment classification of Gujarati–English code-mixed text.
Chirag D. Shah, Shailesh A. Chaudhari· International journal of com...· 0 citations
This proposed work addresses the persistent challenges of data sparsity, linguistic diversity, and limited annotated resources that hinder sentiment analysis in regional Indian languages by proposing a lightweight yet effective machine learning-based framework for automated sentiment classification of Hindi textual dat...
Satyapal Singh, Jarnail Singh, D. S· International journal of com...· 0 citations
A corpus of 10,000 Bangla sentences, manually annotated into four functional categories, namely declarative, interrogative, imperative, and exclamatory is introduced, and Experimental results show that TF-IDF consistently outperforms Word2Vec, likely due to its ability to emphasize discriminative lexical cues associate...
Swapnil Kundu Argha, Abdullah Al Shafi, Rowzatul Zannat et al.· 0 citations
Recommendations are given for choosing a vectorization method depending on the data volume and system performance requirements in real time, and classical methods with linear models demonstrate competitive quality with significantly lower computational costs on small datasets.
O. I. Zakharova, S. Bednyak, Yaroslav Dmitrievich Kanunnikov· Infokommunikacionnye tehnolo...· 0 citations
The proposed approach provides a language-agnostic, context-sensitive segmentation mechanism that can complement language processing methods such as lemmatization, morphological analysis, and stemming and indicate task-dependent and generally limited improvements over traditional token-based word-embedding extraction.
Hayri Volkan Agun· Applied Sciences· 0 citations
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