图 书 馆
Twitter的语义情感分析Semantic Sentiment Analysis of Twitter |
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| 课程网址: | http://videolectures.net/iswc2012_saif_sentiment_analysis/ |
| 主讲教师: | Hassan Saif |
| 开课单位: | 英国开放大学 |
| 开课时间: | 2012-12-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics’ feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. “Apple product”) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment.We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification. |
| 关 键 词: | 情感分析; 情绪分类器; 附加特征 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-09-18：chenxin |
| 阅读次数: | 80 |