The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Sentiment analysis based on improved pre-trained word embeddings
Abstract
Abstract Sentiment analysis is a fast growing area of research in natural language processing (NLP) and text classifications. This technique has become an essential part of a wide range of applications including politics, business, advertising and marketing. There are various techniques for sentiment analysis, but recently word embeddings methods have been widely used in sentiment classification tasks. Word2Vec and GloVe are currently among the most accurate and usable word embedding methods which can convert words into meaningful vectors. However, these methods ignore sentiment information of texts and need a large corpus of texts for training and generating exact vectors. As a result, because of the small size of some corpora, researcher often have to use pre-trained word embeddings which were trained on other large text corpora such as Google News with about 100 billion words. The increasing accuracy of pre-trained word embeddings has a great impact on sentiment analysis research. In this paper, we propose a novel method, Improved Word Vectors (IWV), which increases the accuracy of pre-trained word embeddings in sentiment analysis. Our method is based on Part-of-Speech (POS) tagging techniques, lexicon-based approaches, word position algorithm and Word2Vec/GloVe methods. We tested the accuracy of our method via different deep learning models and benchmark sentiment datasets. Our experiment results show that Improved Word Vectors (IWV) are very effective for sentiment analysis.
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Cites
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Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
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Lexicon-based methods for sentiment analysis
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301
Sentiment analysis algorithms and applications: A survey
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Semantics derived automatically from language corpora contain human-like biases
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Attention-based LSTM for Aspect-level Sentiment Classification
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Cited by
Impact of word embedding models on text analytics in deep learning environment: a review
Asudani, D. S., Nagwani, N. K., & Singh, P. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10419-1 · Cited by 274
Mining opinions from instructor evaluation reviews: a deep learning approach
Onan, A. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22179 · Cited by 270
Generative AI in Marketing: Foundations, Trends, and Future Research Propositions
Prasanna, A., & Kushwaha, B. P. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/5542513 · Cited by 10
Metadata
- Authors
- Rezaeinia, S. M., Rahmani, R., Ghodsi, A., & Veisi, H.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 117
- Pages
- 139-147
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2892137778
- Cited by
- 325
- References
- 44
- FWCI
- 17.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- Rezaeinia, S. M., Rahmani, R., Ghodsi, A., & Veisi, H. (2019). Sentiment analysis based on improved pre-trained word embeddings. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2018.08.044
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