Publicação
Social media cross-source and cross-domain sentiment classification
| Resumo: | Due to the expansion of Internet and Web 2.0 phenomenon, there is a growing interest in the sentiment analysis of freely opinionated text. In this paper, we propose a novel cross-source cross-domain sentiment classification, in which cross-domain labeled Web sources (Amazon and Tripadvisor) are used to train supervised learning models (including two deep learning algorithms) that are tested on typically non labeled social media reviews (Facebook and Twitter). We explored a three step methodology, in which dis- tinct balanced training, text preprocessing and machine learning methods were tested, using two languages: English and Italian. The best results were achieved when using undersampling training and a Convolutional Neural Network. Interesting cross-source classification performances were achieved, in particular when using Amazon and Tripadvisor reviews to train a model that is tested on Facebook data for both English and Italian. |
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| Autores principais: | Zola, Paola |
| Outros Autores: | Cortez, Paulo; Ragno, Costantino; Brentari, Eugenio |
| Assunto: | Convolutional neural network cross-domain data sentiment analysis social media Facebook Twitter |
| Ano: | 2019 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Due to the expansion of Internet and Web 2.0 phenomenon, there is a growing interest in the sentiment analysis of freely opinionated text. In this paper, we propose a novel cross-source cross-domain sentiment classification, in which cross-domain labeled Web sources (Amazon and Tripadvisor) are used to train supervised learning models (including two deep learning algorithms) that are tested on typically non labeled social media reviews (Facebook and Twitter). We explored a three step methodology, in which dis- tinct balanced training, text preprocessing and machine learning methods were tested, using two languages: English and Italian. The best results were achieved when using undersampling training and a Convolutional Neural Network. Interesting cross-source classification performances were achieved, in particular when using Amazon and Tripadvisor reviews to train a model that is tested on Facebook data for both English and Italian. |
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