Authors:
Gerhard Hagerer
1
;
Wing Sheung Leung
1
;
Qiaoxi Liu
1
;
Hannah Danner
2
and
Georg Groh
1
Affiliations:
1
Social Computing Research Group, Department of Informatics, Technical University of Munich, Germany
;
2
Chair of Marketing and Consumer Research, TUM School of Management, Technical University of Munich, Germany
Keyword(s):
Opinion Mining, Topic Modeling, Sentiment Analysis, Cross-lingual, Multi-lingual, Market Research.
Abstract:
User-generated content from social media is produced in many languages, making it technically challenging to compare the discussed themes from one domain across different cultures and regions. It is relevant for domains in a globalized world, such as market research, where people from two nations and markets might have different requirements for a product. We propose a simple, modern, and effective method for building a single topic model with sentiment analysis capable of covering multiple languages simultanteously, based on a pre-trained state-of-the-art deep neural network for natural language understanding. To demonstrate its feasibility, we apply the model to newspaper articles and user comments of a specific domain, i.e., organic food products and related consumption behavior. The themes match across languages. Additionally, we obtain an high proportion of stable and domain-relevant topics, a meaningful relation between topics and their respective textual contents, and an inter
pretable representation for social media documents. Marketing can potentially benefit from our method, since it provides an easy-to-use means of addressing specific customer interests from different market regions around the globe. For reproducibility, we provide the code, data, and results of our studya. ahttps://github.com/apairofbigwings/cross-lingual-opinion-mining
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