Estimating Reputation Polarity on Microblog Posts |
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| Affiliation: | 1. Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco. C/Iván Pavlov, s/n., 28049 Madrid, Spainn;2. Universidad Nacional de Educación a Distancia, Juan del Rosal, nº 10. 28023, Spain;3. Semantia Lab, Bravo Murillo, 38. 28015, Madrid, Spain;1. Department of Computer Science and Software Engineering, International Islamic University, Sector H-10, Islamabad 44000, Pakistan;2. Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, United States;1. CIBER Research Ltd., 1 Westwood Farmhouse, Greenham, Newbury RG14 7RU, United Kingdom;2. Innovation Value Institute, National University of Ireland Maynooth, Irelandn;3. Center for Information and Communication Studies, University of Tennessee, 230 Communications and University Extension Building, 1345 Circle Park, Knoxville, TN 37996-0341, United Statesn;4. School of Information Science, College of Communication and Information, University of Tennessee, 453 Communications Bldg., Knoxville, TN 37996-0341, United Statesn;5. School of Communication Studies, College of Communication and Information, University of Tennessee, 293 Communications Bldg., Knoxville, TN 37996-0341, United Statesn;6. School of Information Sciences, , College of Communication and Information, University of Tennessee, 1340 Circle Park Drive, 423 Communications Bldg, Knoxville, TN 37996-0341, United Statesn |
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| Abstract: | In reputation management, knowing what impact a tweet has on the reputation of a brand or company is crucial. The reputation polarity of a tweet is a measure of how the tweet influences the reputation of a brand or company. We consider the task of automatically determining the reputation polarity of a tweet. For this classification task, we propose a feature-based model based on three dimensions: the source of the tweet, the contents of the tweet and the reception of the tweet, i.e., how the tweet is being perceived. For evaluation purposes, we make use of the RepLab 2012 and 2013 datasets. We study and contrast three training scenarios. The first is independent of the entity whose reputation is being managed, the second depends on the entity at stake, but has over 90% fewer training samples per model, on average. The third is dependent on the domain of the entities. We find that reputation polarity is different from sentiment and that having less but entity-dependent training data is significantly more effective for predicting the reputation polarity of a tweet than an entity-independent training scenario. Features related to the reception of a tweet perform significantly better than most other features. |
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| Keywords: | Social media analysis Online reputation analysis |
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