The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks
Abstract
Summary: Sentiment analysis is one of the major tasks of natural language processing, in which attitudes, thoughts, opinions, or judgments toward a particular subject has been extracted. Web is an unstructured and rich source of information containing many text documents with opinions and reviews. The recognition of sentiment can be helpful for individual decision makers, business organizations, and governments. In this article, we present a deep learning‐based approach to sentiment analysis on product reviews obtained from Twitter. The presented architecture combines TF‐IDF weighted Glove word embedding with CNN‐LSTM architecture. The CNN‐LSTM architecture consists of five layers, that is, weighted embedding layer, convolution layer (where, 1‐g, 2‐g, and 3‐g convolutions have been employed), max‐pooling layer, followed by LSTM, and dense layer. In the empirical analysis, the predictive performance of different word embedding schemes (ie, word2vec, fastText, GloVe, LDA2vec, and DOC2vec) with several weighting functions (ie, inverse document frequency, TF‐IDF, and smoothed inverse document frequency function) have been evaluated in conjunction with conventional deep neural network architectures. The empirical results indicate that the proposed deep learning architecture outperforms the conventional deep learning methods.
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Cites
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Cited by
Sarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208
Systematic reviews in sentiment analysis: a tertiary study
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09973-3 · Cited by 303
Impact of word embedding models on text analytics in deep learning environment: a review
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A Review on Explainability in Multimodal Deep Neural Nets
Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204
Qu, Z., Liu, X., & Zheng, M. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3203791 · Cited by 140
Metadata
- Authors
- Onan, A.
- Venue
- Concurrency and Computation: Practice and Experience
- Publisher
- Wiley
- Published
- 2020-06-29
- Volume
- 33
- Issue
- 23
- ISSN
- 1532-0626 (ISSN-L)
- 1532-0634
- OpenAlex
- W3039503982
- Cited by
- 396
- References
- 29
- FWCI
- 27.37
- Type
- journal article
- Language
- en
- Open access
- closed
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