United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods
By Vassiliki Rentoumi, Stefanos Petrakis, Manfred Klenner, G. Vouros, Vangelis Karkaletsis. 2010
20 citations recorded by the source corpus.
In the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope well with figurative language could not always reach a certain decision about the polarity orientation of sentences, yielding erroneous evaluations. We support the conjecture that these cases bearing mild figurativeness could be better handled by a rule-based system. These two systems, acting complementarily, could bridge the gap between machine learning and rule-based approaches. Experimental results using the corpus of the Affective Text Task of SemEval 07, provide evidence in favor of this direction.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Soujanya Poria
- Erik Cambria
- Percy Liang
- Richard Socher
- Devamanyu Hazarika
- Mohit Bansal
- Noah A. Smith
- Christopher D. Manning
In Natural Language Processing
- Richard Socher
- Soujanya Poria
- Christopher D. Manning
- Samuel R. Bowman
- Noah A. Smith
- Percy Liang
- Mohit Bansal
- Caiming Xiong
Citations in the corpus
- Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank (2013, background): “Several works have explored sentiment compositionality through careful engineering of features or polarity shifting rules on syntactic structures (Polanyi and Zaenen, 2006; Moilanen and Pulman, 2007; Rentoumi et al., 2010; Nakagawa et al., 2010).”
- Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “Thus, researchers introduced the concept of modeling sentiment composition, learned via heuristics and rules, Choi & Cardie [23], hybrid systems, Rentoumi et al.[181], syntactic dependencies, Nakagawa et al. [157], Hutto & Gilbert [78], amongst others.”