Abstract
Opinion-rich information generated by social media users plays a vital role in today’s market economy. The impact exists from understanding the current fashion trend to the product failure anatomy. It helps to decide the plans for the growth of an industry. To address this issue, business analysts want a detailed aspect-based analysis of user opinion. Many researchers either tried a supervised or unsupervised approach for the same. The literature review showed that the weak structuring of the corpus impacts the outcome of the state-of-the-art method. This paper illustrates the semi-supervised way to extract and summarize the aspect and sentiments associated with the user reviews. We proposed a mechanism to learn aspect-related terms (ARTs) for the seed aspect terms (ATs) from the corpus. We used the customer review dataset and SemEval Reviews-English to test the working and performance of our system. The results show that the proposed method achieves a recall upto 0.88 for the review corpus.
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Nandedkar, S.C., Patil, J.B. (2022). SeAbOM: Semi-supervised Learning for Aspect-Based Opinion Mining. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications . Lecture Notes in Networks and Systems, vol 288. Springer, Singapore. https://doi.org/10.1007/978-981-16-5120-5_36
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DOI: https://doi.org/10.1007/978-981-16-5120-5_36
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