Abstract
Citation represents the relationship between the cited and the citing document and vice versa. Citations are widely used to measure the different aspects of knowledge-based achievements such as institutional ranking, author ranking, the impact factor of the journal, research grants, and peer judgments. A fair evaluation of research required a quantitative and qualitative assessment of citations. To perform the qualitative analysis of citations, researchers tried to classify the citations into binary classes (i.e., important and non-important). To perform this task, researchers used metadata, content, citations count, cue words or phrases, sentiment analysis, keywords, and machine learning approaches for citation classification. However, the state-of-the-art results of binary classification are inadequate for the calculation of different aspects of the researcher and their work. Therefore, this research proposed an in-text citation sentiment analysis-based approach for binary classification which effectively enhanced the results of the state-of-the-art. In this research, different machine learning-based models are evaluated to determine the in-text citations sentiments. These sentiment results are further used for positive-negative, and neutral citation counts. Furthermore, the scores of cosine similarity between paper citation pairs are also calculated and used as a feature. This sentiment and cosine similarity scores are further used as features in binary classification. The classification is performed through SVM, KLR, and Random Forest. The proposed approach is evaluated and compared with two state-of-the-art approaches on the benchmark dataset. The proposed approach can achieve 0.83 f-measure with the improvement of 13.6% for dataset 1 and 0.67 with an improvement of 8% for dataset two with a random forest classification model.