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An Improved Cross-Domain Sentiment Analysis Based on a Semi-Supervised Convolutional Neural Network

An Improved Cross-Domain Sentiment Analysis Based on a Semi-Supervised Convolutional Neural Network
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Author(s): Lap-Kei Lee (Hong Kong Metropolitan University, Hong Kong), Kwok Tai Chui (Hong Kong Metropolitan University, Hong Kong), Jingjing Wang (Hong Kong Metropolitan University, Hong Kong), Yin-Chun Fung (Hong Kong Metropolitan University, Hong Kong) and Zhanhui Tan (Hong Kong Metropolitan University, Hong Kong)
Copyright: 2022
Pages: 16
Source title: Data Mining Approaches for Big Data and Sentiment Analysis in Social Media
Source Author(s)/Editor(s): Brij B. Gupta (Asia University, Taiwan), Dragan Peraković (University of Zagreb, Croatia), Ahmed A. Abd El-Latif (Menoufia University, Egypt) and Deepak Gupta (LoginRadius Inc., Canada)
DOI: 10.4018/978-1-7998-8413-2.ch007

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Abstract

The dependence on Internet in our daily life is ever-growing, which provides opportunity to discover valuable and subjective information using advanced techniques such as natural language processing and artificial intelligence. In this chapter, the research focus is a convolutional neural network for three-class (positive, neutral, and negative) cross-domain sentiment analysis. The model is enhanced in two-fold. First, a similarity label method facilitates the management between the source and target domains to generate more labelled data. Second, term frequency-inverse document frequency (TF-IDF) and latent semantic indexing (LSI) are employed to compute the similarity between source and target domains. Performance evaluation is conducted using three datasets, beauty reviews, toys reviews, and phone reviews. The proposed method enhances the accuracy by 4.3-7.6% and reduces the training time by 50%. The limitations of the research work have been discussed, which serve as the rationales of future research directions.

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