Propagating sentiment signals for estimating reputation polarity |
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| Institution: | 1. Faculty of Informatics, Università della Svizzera italiana (USI), Lugano, Switzerland;2. NLP & IR Group, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain;1. School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA;2. Department of Computing and Mathematics University of São Paulo, Ribeirão Preto, SP, Brazil;3. Institute of Science and Technology Federal University of São Paulo, São José dos Campos, SP, Brazil;4. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada;1. School of Data and Computer Science, Sun Yat-sen University, China;2. School of Computing Science, University of Glasgow, Glasgow, UK;3. School of Computer Science, The University of Adelaide, Adelaide, Australia;1. University of North Carolina at Chapel Hill, United States;2. Data Science, Facebook 1 Hacker Way Menlo Park, CA 94025, United States;3. User Experience and Customer Insights, NetBrain 15 Network Drive Burlington, MA, 01803, United States;1. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China;2. School of Business, Yunnan University of Finance and Economics, Kunming 650221, China;3. School of Business and Management, Shanghai International Studies University, Shanghai 201620, China |
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| Abstract: | The emergence of social media and the huge amount of opinions that are posted everyday have influenced online reputation management. Reputation experts need to filter and control what is posted online and, more importantly, determine if an online post is going to have positive or negative implications towards the entity of interest. This task is challenging, considering that there are posts that have implications on an entity's reputation but do not express any sentiment. In this paper, we propose two approaches for propagating sentiment signals to estimate reputation polarity of tweets. The first approach is based on sentiment lexicons augmentation, whereas the second is based on direct propagation of sentiment signals to tweets that discuss the same topic. In addition, we present a polar fact filter that is able to differentiate between reputation-bearing and reputation-neutral tweets. Our experiments indicate that weakly supervised annotation of reputation polarity is feasible and that sentiment signals can be propagated to effectively estimate the reputation polarity of tweets. Finally, we show that learning PMI values from the training data is the most effective approach for reputation polarity analysis. |
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