CluSent – Combining Semantic Expansion and De-Noising for Dataset-Oriented Sentiment Analysis of Short Texts

Author:

Viegas Felipe1ORCID,Canuto Sergio2ORCID,Cunha Washington1ORCID,França Celso1ORCID,Valiense Claudio1ORCID,Rocha Leonardo3ORCID,Gonçalves Marcos André1ORCID

Affiliation:

1. Universidade Federal de Minas Gerais, Brazil

2. Instituto Federal de Goiás, Brazil

3. Universidade Federal de São João del-Rei, Brazil

Funder

CNPq

Fapemig

Google Research Awards

CAPES

AWS

Publisher

ACM

Reference24 articles.

1. Mohamad Alissa Issa Haddad Jonathan Meyer Jade Obeid Kostis Vilaetis Nicolas Wiecek and Sukrit Wongariyakavee. 2021. Sentiment Analysis for Open Domain Conversational Agent. arxiv:2101.00675 [cs.AI] Mohamad Alissa Issa Haddad Jonathan Meyer Jade Obeid Kostis Vilaetis Nicolas Wiecek and Sukrit Wongariyakavee. 2021. Sentiment Analysis for Open Domain Conversational Agent. arxiv:2101.00675 [cs.AI]

2. On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study

3. Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805 (2018). https://arxiv.org/abs/1810.04805 Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805 (2018). https://arxiv.org/abs/1810.04805

4. Word co-occurrence features for text classification

5. Alec Go , Richa Bhayani , and Lei Huang . 2009. Twitter sentiment classification using distant supervision. Processing 150 (01 2009 ). Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter sentiment classification using distant supervision. Processing 150 (01 2009).

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