Sentiment Analysis of Chinese Tourism Review Based on Boosting and LSTM
| Autor: | Weifeng Zhang, Pengfei Liu, Duxian Nie, Kejing He, Xiaxu He, Zhirui Huang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Boosting (machine learning)
business.industry Computer science Deep learning 05 social sciences Sentiment analysis 050801 communication & media studies Machine learning computer.software_genre 0508 media and communications 0502 economics and business 050211 marketing Artificial intelligence business computer Tourism |
| Zdroj: | 2019 International Conference on Communications, Information System and Computer Engineering (CISCE). |
| DOI: | 10.1109/cisce.2019.00154 |
| Popis: | Sentiment analysis is a hot topic in recent years' Natural Language Processing Research, traditional practices are through machine learning, current research is based on deep learning. Most models generally do not consider the characteristic of network data while in modeling procedure. Considering the tourism comments emotion distribution is not balanced, and adding distribution information of comments to the model will play an important role in the effect, this paper proposed a boosting based LSTM model that can well model the comment text. We have done the evaluation on the related data set, and the experimental results show that the model in this paper has achieved good results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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