The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Deep Convolution Neural Networks for Twitter Sentiment Analysis
Open accessAbstract
Twitter sentiment analysis technology provides the methods to survey public emotion about the events or products related to them. Most of the current researches are focusing on obtaining sentiment features by analyzing lexical and syntactic features. These features are expressed explicitly through sentiment words, emoticons, exclamation marks, and so on. In this paper, we introduce a word embeddings method obtained by unsupervised learning based on large twitter corpora, this method using latent contextual semantic relationships and co-occurrence statistical characteristics between words in tweets. These word embeddings are combined with n-grams features and word sentiment polarity score features to form a sentiment feature set of tweets. The feature set is integrated into a deep convolution neural network for training and predicting sentiment classification labels. We experimentally compare the performance of our model with the baseline model that is a word n-grams model on five Twitter data sets, the results indicate that our model performs better on the accuracy and F1-measure for twitter sentiment classification.
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Natural Language Processing (almost) from Scratch
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A Convolutional Neural Network for Modelling Sentences
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Cited by
Naseem, U., Razzak, I., Khan, S. K., & Prasad, M. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3434237 · Cited by 194
Metadata
- Authors
- Jianqiang, Z., Xiaolin, G., & Xuejun, Z.
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 23253-23260
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2781487490
- Cited by
- 482
- References
- 34
- FWCI
- 35.08
- Type
- journal article
- Language
- en
- Open access
- gold
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