## 6 Conclusions  
Sentiment analysis of social media reviews is a difficult task due to sparsity and high dimensionality of word vectors representing the text. Use of sentiment lexicon and word embedding algorithms can improve sentiment analysis models for text reviews. In this context, we proposed a sentiment analysis model, named LeBERT, based on sentiment lexicon, N-grams, BERT word embedding, and CNN. In the model, a section of a document or a sentence where sentiment information can be highly found is selected using sentiment lexicon and word N-grams, and then the words are vectorized using the BERT word embedding algorithm. A CNN classifier is then used to classify the input vector into a sentiment class. To validate the performance of the proposed LeBERT model, original Word2 Vec, Glove, and BERT word embeddings were used as baseline models on three benchmark sentiment datasets. From the experimental results, use of sentiment lexicon significantly reduces the dimension of the input vector, thus improving efficiency of sentiment analysis models. Secondly, integration of sentiment lexicon and N-grams with BERT embedding algorithm yields a better representative word vector, hence increasing the predictive performance of the resultant sentiment analysis model. The results also indicated that sentiment lexicon with BERT (through LeBERT model) outperformed other word embedding algorithms.  
This paper had some limitations. The designed model utilized convolutional neural network (CNN) only. In the future, the LeBERT embedding model could be implemented and evaluated in other neural networks, such as long short-term memory (LSTM). Our  
Figure 4: Sentiment prediction accuracy using various embedding models.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Embedding Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** \\ \hline Glove & 79.00 & 79.00 & 79.65 & 79.32 \\ Le-Glove & 79.60 & 80.00 & 80.45 & 80.22 \\ Word2 Vec & 79.50 & 79.50 & 80.25 & 79.87 \\ Le-Word2 Vec & 81.50 & 81.50 & 82.05 & 81.77 \\ BERT & 81.72 & 81.75 & 82.04 & 81.89 \\ LeBERT & 82.40 & 82.40 & 82.64 & **82.52** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Sentiment classification prediction under Amazon dataset (products reviews).  
proposed model was tested and found to be effective in binary sentiment classification, where sentiment lexicon was used. It would be interesting to evaluate the model on other text classification tasks where other types of lexicons are used.  
Conceptualization, J.M., W.M. and G.O.; Data curation, J.M.; Formal analysis, G.O. and W.M.; Investigation, J.M., G.O. and W.M.; Methodology, J.M., G.O. and W.M.; Supervision, G.O. and W.M.; Writing--original draft, J.M.; Writing--review and editing, G.O. and W.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset used in the experiments is publicly available from [[https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences](https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences)]([https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences](https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences)) accessed on 6 October 2022. The dataset was compiled by [PERSON] et al. [40], which is cited in this research work.  
The authors declare that they have no conflict of interest.