.png){kind=icon}
返回
Multilingual emotion classification using supervised learning: Comparative experiments
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ipm.2016.12.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The importance of emotion mining is acknowledged in a wide range of new applications, thus broadening the potential market already proven for opinion mining. However, the lack of resources for languages other than English is even more critical for emotion mining. In this article, we investigate whether Multilingual Sentiment Analysis delivers reliable and effective results when applied to emotions. For this purpose, we developed experiments involving machine translations over corpora originally written in two languages. Our experimental framework for emotion classification assesses variations on (i) the language of the original text and its translations; (ii) strategies to combine multiple languages to overcome losses due to translation; (iii) options for data pre-processing (tokenization, feature representation and feature selection); and (iv) classification algorithms, including meta classifiers. The results show that emotion classification performance improve significantly with the use of texts in multiple languages, particularly by adopting a stacking of weak monolingual classifiers. Our study also sheds light into the impacts of data preparation strategies and their combination with classification algorithms, and compares differences between polarity and emotion classification according to the same experimental settings. (c) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Sentiment analysis
Multilingual sentiment analysis
Emotion mining
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
6.9
论文数:
5.2K
被引数:
1.4W
机构
引用论文
.png){kind=icon}
分享收藏Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text他们不同吗？文本中的情感、感觉、情感和观点检测
PREAI
分享收藏分享收藏
查看更多内容