Abstract
In this paper, we propose a multilevel feature representation method that combines word-level features, such as German morphology and slang, and sentence-level features, such as special symbols and English-translated sentiment information, and build a deep learning model for German sentiment classification based on the self-attentive mechanism, in order to address the characteristics of German social media texts that are colloquial, irregular, and diverse. Compared with the existing studies, this model not only has the most obvious improvement effect but also has better feature extraction and classification ability for German emotion.
Highlights
Big data in the context of the Internet has become an important force in promoting digital humanities research, and the analysis of sentiment tendencies in social media texts such as Twitter has been a hot topic of research in natural language processing [1]
Compared to English, fewer studies have been conducted on German sentiment dictionaries, and the existing work either focuses on topics related to fixed domains [9] or applies only to targetoriented sentiment classification [23], which has limitations when analyzing German social media texts. e dictionary [24] contains 16,057 sentiment entries in the general domain, but it is still difficult to cover the complex German language morphology and new words on the Internet and only coarsely classifies words into 4 levels based on sentiment intensity, without precisely distinguishing the detailed sentiment differences between entries
In order to effectively combine the ability of CNN model to capture local features of multidimensional data, the advantage of RNN model to extract long-term dependencies in sequences, and the feature of self-attentive mechanism to focus on important information, and improve the German sentiment classification, this paper proposes a hybrid CNN-BiLSTM deep learning model ACBM based on self-attentive mechanism
Summary
Big data in the context of the Internet has become an important force in promoting digital humanities research, and the analysis of sentiment tendencies in social media texts such as Twitter has been a hot topic of research in natural language processing [1]. As one of the main ways for people to communicate and express their emotions, social media generates a large amount of short texts in German with subjective emotions every day, and it is beneficial to summarize, analyze, and reason about the emotional information contained in them for making business decisions, analyzing political opinions, and predicting social trends in related countries [3]. Some studies have attempted to obtain the results of sentiment analysis of German English translations with the help of English-related tools [5]. (2) Social media texts are characterized by colloquialism, slang, irregular language, and lack of obvious contextual information when conveying information, evaluating objects, or expressing opinions, which makes it difficult to obtain satisfactory results by using common sentiment analysis methods [7]. To address the above difficulties and the characteristics of the German network language, this paper uses deep learning methods to accomplish the following work.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
