Universiti Teknologi Malaysia Institutional Repository

Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion

Abdi, Asad and Shamsuddin, Siti Mariyam and Hasan, Shafaatunnur and Piran, Jalil (2019) Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion. Information Processing and Management, 56 (4). pp. 1245-1259. ISSN 0306-4573

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Official URL: http://dx.doi.org/10.1016/j.ipm.2019.02.018


Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge amount of reviews, sentiment analysis plays a basic role to extract significant information and overall sentiment orientation of reviews. In this paper, we present a deep-learning-based method to classify a user's opinion expressed in reviews (called RNSA). To the best of our knowledge, a deep learning-based method in which a unified feature set which is representative of word embedding, sentiment knowledge, sentiment shifter rules, statistical and linguistic knowledge, has not been thoroughly studied for a sentiment analysis. The RNSA employs the Recurrent Neural Network (RNN) which is composed by Long Short-Term Memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about the word are vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the following drawbacks: words with similar semantic context but opposite sentiment polarity; contextual polarity; sentence types; word coverage limit of an individual lexicon; word sense variations. To verify the effectiveness of our work, we conduct sentence-level sentiment classification on large-scale review datasets. We obtained encouraging result. Experimental results show that (1) feature vectors in terms of (a) statistical, linguistic and sentiment knowledge, (b) sentiment shifter rules and (c) word-embedding can improve the classification accuracy of sentence-level sentiment analysis; (2) our method that learns from this unified feature set can obtain significant performance than one that learns from a feature subset; (3) our neural model yields superior performance improvements in comparison with other well-known approaches in the literature.

Item Type:Article
Uncontrolled Keywords:Deep learning, Sentiment analysis
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:88503
Deposited By: Widya Wahid
Deposited On:15 Dec 2020 08:19
Last Modified:15 Dec 2020 08:19

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