Text Sentiment Analysis Based on Transformer and Augmentation

Abstract

With the development of Internet technology, social media platforms have become an indispensable part of people’s lives, and social media have been integrated into people’s life, study, and work. On various forums, such as Taobao and Weibo, a large number of people’s footprints are left all the time. It is these chats, comments, and other remarks with people’s emotional evaluations that make up part of public opinion. Analysis of this network public opinion is conducive to maintaining the peaceful development of society. Therefore, sentiment analysis has become a hot research field and has made great strides as one of the hot topics in the field of natural language processing. Currently, the BERT model and its variants have achieved excellent results in the field of NLP. However, these models cannot be widely used due to huge demands on computing resources. Therefore, this paper proposes a model based on the transformer mechanism, which mainly includes two parts: knowledge distillation and text augmentation. The former is mainly used to reduce the number of parameters of the model, reducing the computational cost and training time of the model, and the latter is mainly used to expand the task text so that the model can achieve excellent results in the few-sample sentiment analysis task. Experiments show that our model achieves competitive results.

Augmenting Azoles with Drug Synergy to Expand The Antifungal Toolbox

“…Effective against biofilms [211] butylphthalide Effective against biofilms [212] glabridin [213] chito-oligosacch. [214] oridonin [215] Crude Ess.…”

“…Glabridin from liquorice root synergised with voriconazole in all A. fumigatus strains tested [213]. Several oil extracts displayed excellent anti-biofilm activity when combined with fluconazole, including chito-oligosaccharides, tyrosol from olive oil, allyl isothiocyanate from mustard oil and butylphthalide from celery oil [210][211][212]214]. Some crude essential oils have also been investigated for potential synergy, including oils from Indian frankincense, tea tree, sea buckthorn and guava leaf.…”

Automatic Sentiment Annotation of Idiomatic Expressions for Sentiment Analysis Task

“…To capture intricate linguistic patterns, language models have frequently employed the transformer acting as the fundamental design of the attention mechanism [11]. It is true that these pretrained language models have high requirements for the quality of the training dataset and rely on large-scale data sets, but it is also true that these models excel in a range of NLP tasks [7,11].…”

“…The last method, known as back-translation. In this method a phrase or a sentence is translated into another language (forward translation), and then translating the results back into the original language [7].…”

“…After [5] successfully applied CNN to sentiment classification task and reported positive results, the authors of [6] presented a VDCNN model based on deep convolutional network method [6]. Traditionally, neural network methods begin their model parameters at random before training them using optimization methods like back propagation and gradient descent [7]. However, the use of neural networkbased deep learning for natural language processing encountered the following issues before the development of pre-training techniques: First of all, complex models needed deep learning models that were too primitive at that time.…”