A comprehensive topic-model based hybrid sentiment analysis system

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  • Nowadays, Twitter sentiment analysis is drawing a lot of attention due to its potential to drive decision making in a variety of domains. However, the trend that publicly available training datasets are becoming less available, the difficulty in determining topic numbers for topic model-based approach, and a lack of data level discussion about how to utilize the proposed models day-to-day to drive applications are still the remained concerns. To solve these problems, we firstly offer a new method to collect and build Twitter training dataset based on noisy labels; In addition, we proposed a topic-model based hybrid sentiment classification model by using our self-collected tweets, which utilizes three different topic models and coherence score to choose the best topic model in an automated way; Last but not least, a use case is illustrated to show how to apply our pipeline in a daily basis to solve real business problems.

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  • Copyright © 2019 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
Date Created
  • 2019


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