Context- and sentiment-aware machine learning models for sentiment analysis

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2023-1-24

Author

Deniz Kızılöz, Firdevsi Ayça

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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With the advances in information technologies, the amount of available data on web sources where people express their opinions increases continually. Sentiment analysis supports decision-makers in gaining insights from massive heaps of data. It has gained much attraction recently as it has proven to be a practical tool in a wide range of areas, including monitoring public opinion. Nevertheless, sentiment analysis research is still facing some challenges. One of the main challenges is the irrelevant and redundant features in the data. Such features not only increase the search space enormously but also disrupt the context awareness of the model. Another main challenge is the lack of domain-agnostic models for the sentiment analysis tasks as an existing model may not be the best fit for another domain in terms of context. Although deep learning models provide high-performance results, they require a massive amount of labeled data. However, obtaining a sufficient amount of labeled data is often impractical. In this thesis, we propose four models to remedy the aforementioned drawbacks. Our first model extracts the most informative features in the data for sentiment analysis. The second one constructs a context-refined word embedding model. The third model transfers the knowledge in pre-trained models to a new domain without the necessity of labeled data. The last one is a feature ensemble model that builds a pool of varying features for sentiment analysis. To verify the effectiveness of our models, we held extensive experiments on three benchmark datasets. Moreover, we introduced two novel datasets consisting of thousands of sentence and sentiment class pairs. Experiment results demonstrated that the proposed models yield performance improvements.

Subject Keywords

Natural language processing, Sentiment analysis, Machine learning

URI

https://hdl.handle.net/11511/102575

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

F. A. Deniz Kızılöz, “Context- and sentiment-aware machine learning models for sentiment analysis,” Ph.D. - Doctoral Program, Middle East Technical University, 2023.