## 2 Related Work  
The analysis of the citation sentiments for scientific papers is a newly emerging problem. In order to automatically identify the sentiment polarity (positive, negative, or neutral) in the citation, an experiment was conducted by [PERSON] using the existing features, which include; n-grams, 3-grams, lexical features, dependency relations, negations, and sentence splitting features on the citation sentiment corpus. The results shown in the research were that dependency relations and the 3-grams show the best results of all [9]. The objective of the citation sentiment analysis is to determine different patterns; with the bulk data availability, it is a need to extract useful information. The main aim was to automate the method for obtaining the sentiment polarity--either positive, negative, or neutral. Sentiment analysis is the need to determine the citation's sentiment polarity. Different trends were discussed, and an effective approach was recommended [18]. Citation sentiment analysis is performed to analyze the sentiments of the author towards the cited paper. A survey to analyze the sentiments of scientific citations was made by the authors, in which an introduction to the sentiment analysis process was given, the challenges faced by the previously proposed methods were discussed and presented, and an analysis was made. The main focus of the research was to identify the challenges that the recently proposed methods are facing, and how effective they are in citation sentiment analysis; the used classifications were also analyzed. It was concluded that machine learning is the most common method used for the analysis of the citation sentiments in scientific papers, and it was also seen that, because of the limitation of this method, deep learning methods can be used to effectively analyze the sentiment polarity of citations in scientific papers [15]. Citation sentences were used to analyze citation sentiments. The existing citation corpus was used. The corpus comprises 8736 citation sentences. To clean the data, different normalization rules were used. The classification was performed on six different classifiers of machine learning. Different evaluation parameters, such as accuracy, precision, recall, and F1 score, were used to evaluate the accuracy of the models. To improve the model's accuracy, different approaches were recommended, such as n-gamming, stop words removal, lemmatization, stemming, etc. The accuracy was improved by 9% using these methods [19]. With the increase in the amount of online data, especially textual data, sentiment analysis has become an emerging field. The classification of sentences or documents is completed by either considering the objective or the subjective textual data. Then, the classifications are collected and the sentiment analysis technique is applied to it to check the sentiment polarity (positive, negative, or neutral) of the text to predict the sentiments of the citation text. Several text preprocessing techniques are available that are used to preprocess the textual data to obtain a dataset that is more clear and concise and that comprises only useful features, excluding the useless features. The polarity of the cited text is analyzed by using the machine learning classifiers [20,21]. However, many state-of-the-art approaches are present, and are used to evaluate the accuracy of the models and to extract the sentiments expressed in the cited text. A method for determining the in-text citations using the citation sentiment analysis methodology was proposed, in which different machine learning models were used to first train the dataset. Then, testing was performed on unseen datasets to check the accuracy of the machine learning models in the case of in-text citations. Random forest, support vector machine, and kernel logistic regression models were used in the research to evaluate the accuracy [22]. A literature review reveals that many machine learning models and manual feature engineering techniques have been analyzed by many researchers.