Deep Convolution Neural Networks for Twitter Sentiment Analysis

By Jianqiang Zhao, Xiaolin Gui, Xuejun Zhang. IEEE Access, 2018

369 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/78b1dc7995f9f9b7ee7ef6e193374b9fe3487d30

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In Artificial Intelligence

1. Guixian Xu
2. Xu Wu
3. Xiaoyu Qiu
4. Yueting Meng
5. Ziheng Yu

Citations in the corpus

• Sentiment Analysis of Comment Texts Based on BiLSTM (2019, background): “In natural language processing, words in sentences or documents are usually used as features [24]–[26].”
• Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning (2020, positive): “Jianqiang et al. [38] used the contextual semantic features and the co-occurrence statistical features of the words in the tweet and the n-gram feature input convolutional neural network to analyze the sentiment polarity.”