@inproceedings{,
    author = {Kersting, Joschka; Geierhos, Michaela},
    title = {Aspect Phrase Extraction in Sentiment Analysis with Deep Learning},
    editor = {},
    booktitle = {Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) :  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)},
    series = {},
    journal = {},
    address = {},
    publisher = {SCITEPRESS},
    edition = {},
    year = {2020},
    isbn = {},
    volume = {},
    number =  {},
    pages = {391-400},
    url = {},
    doi = {},
    keywords = {Deep Learning ; Natural Language Processing ; Aspect-based Sentiment Analysis},
    abstract = {This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.},
    note = {},
    
    
    
}