| Title | Comparison of Full-Text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Nazemi, Kawa, Klepsch, Maike J., Burkhardt, Dirk, Kaupp, Lukas |
| Conference Name | 2020 24th International Conference Information Visualisation (IV) |
| Date Published | sep |
| Keywords | Coherence, composability, Data Science, Human Behavior, human factors, information science, Large scale integration, Libraries, Market research, Metrics, natural language processing, pubcrawl, Scalability, text analytics, visual analytics, Visual Trend Analytics |
| Abstract | Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Analytics viewpoint in this context is, do abstracts of scientific publications provide a similar analysis capability compared to their corresponding full-texts? This would allow to extract a mass amount of text documents in a much faster manner. We compare in this paper the topic extraction methods LSI and LDA by using full text articles and their corresponding abstracts to obtain which method and which data are better suited for a Visual Analytics system for Technology and Corporate Foresight. Based on a easy replicable natural language processing approach, we further investigate the impact of lemmatization for LDA and LSI. The comparison will be performed qualitative and quantitative to gather both, the human perception in visual systems and coherence values. Based on an application scenario a visual trend analytics system illustrates the outcomes. |
| DOI | 10.1109/IV51561.2020.00065 |
| Citation Key | nazemi_comparison_2020 |
Comparison of Full-Text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing