A Unified Deep Learning Framework for Sentiment Analysis of Reviews

Document Type

Book Chapter

Publication Title

Studies in Computational Intelligence

Abstract

Online user-generated content is increasing rapidly on a daily basis, along with the expansion of social media and e-commerce activities. This contains users’ personal views and opinions in regard to various products, activities, news, ideas, politics, etc., in textual form. Automated analysis of these opinions’ tone helps to make decisions and devise strategies. This activity is known as sentiment analysis or opinion mining and is essential for text processing. We propose a unified framework as a sequential end-to-end process of efficient sentiment analysis. The framework uses a combination of deep learning models to better and efficiently assess the text based on spatial and temporal context and linguistic interrelationships. The proposed framework first improves word embeddings to represent sentiments better. These improved embeddings are used to assess the usefulness of reviews. The identified useful and relevant reviews are retained in the dataset, and the unhelpful reviews are discarded to reduce the dataset size. The reduced dataset is then analyzed for subjectivity sentence-wise, using the improved word embeddings for feature representation. The identified objective sentences are filtered from the reviews, leaving only the subjective sentences. Finally, polarity classification is performed on these obtained reviews to identify their overall sentiment as positive or negative. This framework is tested on two datasets of different domains. The superiority of the results is demonstrated by comparing them to the state-of-the-art sentiment analysis techniques. The framework achieves better performance and outperforms the existing methods.

First Page

25

Last Page

54

DOI

10.1007/978-981-97-5204-1_2

Publication Date

1-1-2024

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