## 5 Conclusions  
This work presents a novel ACL-SA model to tackle the lack of semantic information, high dimensionality, and overfitting problems. Our model is a joined CNN- bidirectional-LSTM architecture that uses a combination of TF-IDF weighting and pre-trained Glove word embeddings. In addition, we engaged three CNN layers to extract contextual features with Max-pooling at the output of the CNNs layer for dimensionality reduction of feature  
Figure 4: SD4A loss rate change on training and validation data (**a**); US-Airline loss rate change on training and validation data (**b**); Sentiment140 loss rate change on training and validation data (**c**); Sentiment140-MV loss rate change on training and validation data (**d**).  
space. Furthermore, an attention mechanism is employed at the end of the CNN layers to put more or less attention into different words. Collectively, the bidirectional LSTM network serves as a temporal feature and updates the CNN output layer on the past and future sentiment representation. Finally, we employed a dense and fully connected layer with Relu activation and sigmoid function to transform the vector into sentiment polarity classification. In addition, we present TF-IDF weighting and pre-trained word embedding to extract significant word representation to keep the word order information of the Twitter data. Gaussian Noise and Gaussian Dropout were subsequently used on the input layer to overcome overfitting. Experiments were conducted on four different Twitter datasets to analyze the performance of the proposed model. We engaged the most recent deep learning research models for sentiment analysis for comparison. The proposed model's performance achieved a magnitude of 0.9453, 0.993, 0.94, and 0.8712 on the SD4A, Sentiment140-MV, US-Airline, and Sentiment140 datasets--a significantly improved accuracy compared to baseline methods results. Wilcoxon test results verified the significant difference between the proposed model and baselines. The hypothesis has been validated (_p_-value < 0.05) for all methods pair tests with our proposed method. In the future, we will work to expand our model for other languages.  
Conceptualization, resources, M.K. and G.L.; methodology, implementation, validation, investigation, data curation, visualization, M.K.; formal analysis, paper draft preparation, writing and editing, M.K. and M.A.; supervision, funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Innovation and Development of Shanghai Industrial Internet (Grant No. XX-GYHL-01-19-2527).  
Not applicable.  
The datasets presented in this study can be found in the following links: Sentiment140- [[http://help.sentiment140.com/for-students](http://help.sentiment140.com/for-students)]([http://help.sentiment140.com/for-students](http://help.sentiment140.com/for-students)); US-Airline-[[https://www.kaggle.com/crowdflower/twitter-airline-sentiment](https://www.kaggle.com/crowdflower/twitter-airline-sentiment)]([https://www.kaggle.com/crowdflower/twitter-airline-sentiment](https://www.kaggle.com/crowdflower/twitter-airline-sentiment)); SD4A-[[https://www.kaggle.com/kamyab20/sentiment-dataset-for-afghanistan-sd4a](https://www.kaggle.com/kamyab20/sentiment-dataset-for-afghanistan-sd4a)]([https://www.kaggle.com/kamyab20/sentiment-dataset-for-afghanistan-sd4a](https://www.kaggle.com/kamyab20/sentiment-dataset-for-afghanistan-sd4a)); Sentiment14-MV--[[https://www.kaggle.com/kamyab20/sentiment140](https://www.kaggle.com/kamyab20/sentiment140) mv]([https://www.kaggle.com/kamyab20/sentiment140](https://www.kaggle.com/kamyab20/sentiment140) mv); the code could be provided by request.  
The authors declare no conflict of interest.