Abstract
Twitter sentiment analysis has emerged and become interesting in many field that involves social networks. Previous researches have assumed the problem as a tweet-level classification task where it only determines the general sentiment of a tweet. This paper proposed hybrid approach to analyze aspect-based sentiments for tweets. We conducted several experiments to identify explicit and implicit aspects which is crucial for aspect-based sentiment analysis. The hybrid approach between association rule mining, dependency parsing and Sentiwordnet is applied to solve this aspect-based sentiment analysis problem. The performance is evaluated using hate crime domain and other benchmark dataset in order to evaluate the results and the finding can be used to improve the accuracy for the aspect-based sentiment classification.
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Acknowledgments
The Universiti Teknologi Malaysia (UTM) under Research University funding vot number 02G31 and Ministry of Higher Education (MOHE) Malaysia under vot number 4F550 are hereby sincerely acknowledged for providing the research fundings to complete this research.
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Zainuddin, N., Selamat, A., Ibrahim, R. (2016). Improving Twitter Aspect-Based Sentiment Analysis Using Hybrid Approach. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_15
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DOI: https://doi.org/10.1007/978-3-662-49381-6_15
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