The word embedding models have achieved state-of-the-art results in a variety of natural language processing tasks. Whereas, current word embedding models mainly focus on the rich semantic meanings while are challenged by capturing the sentiment information. For this reason, we propose a novel sentiment word embedding model. In line with the working principle, the parameter estimating method is highlighted. On the task of semantic and sentiment embeddings, the parameters in the proposed model are determined by using both the maximum likelihood estimation and the Bayesian estimation. Experimental results show the proposed model significantly outperforms the baseline methods in sentiment analysis for low-frequency words and sentences. Besides, it is also effective in conventional semantic and sentiment analysis tasks.
This paper proposes a method to learn word embedding by incorporating additional sentiment information. The proposed method extends from D-GloVe by adding the probability of positive sentiment to the loss function. The paper presents three experiments: word similarity, word-level sentiment analysis, and sentence-level sentiment analysis. The experiments show that the method performs comparably with other baseline methods and outperforms in the low-frequency sentence setting (i.e. sentence containing lower frequency words).
I recommend rejecting this paper because (1) the writing is unclear and hard to follow, and (2) the experiment results are not convincing.
From what I can understand in the model part, there are many clarifications needed, not to mention the writing style. I think the re-derivations of GloVe and D-GloVe are not helpful as they cloud the main contribution of the paper. The author should clearly highlight the differences between the main subjects of the experiment: MLESWE and BESWE. In addition, it is not clearly motivated why we need Dirichlet prior for the sentiment variable.
While the claim is to learn better embeddings for rare words, the experiments show that the proposed methods have similar results to the previous work. The only gain we can observe is in the sentence-level experiments in which other factors could affect the performance. Thus, it is hard to draw a supportive conclusion.
Finally, the writing quality must be improved. The paper contains a lot of unrelated and redundant texts (it could be that I could follow the paper). 
1. I do not think eq 2 is a representative of how the paper train the model, nor attempt to compare with. 
2. As mention earlier, in section 3.2 and 3.3, the re-derivation is not particularly helpful. I think the paper should put more emphasis on the novelty of the work. 
3. Plots in the experiment results are illegible. Tables should be more suitable for Figures 1, 2, and 3. 
I urged the authors to revise this paper and make sure it follows the formatting guideline, especially the citations. Finally, I'd recommend the authors have a professional writer (English) review the paper before submission.