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The excessive amount of digital information has made it crucial to extract the relevant information. This hinders researchers in finding documents pertaining to their research. There exist various state-of-the-art techniques, such as co-citation, bibliographic coupling, and their recent extensions like citation proximity analysis and citation order analysis, that recommend the relevant documents against the posed query. Most of these approaches are statistical in nature and thus can further be extended by incorporating some semantics to enhance the results. In this paper, we present an extension of a co-citation-based technique to identify the most relevant documents to co-cited document(s). The proposed system explores in-text citation frequencies and in-text citation patterns of co-cited documents within the different logical sections of cited-by papers. Furthermore, we have evaluated the proposed approach with the co-citation approach and citation proximity analysis (CPA) approach on a dataset acquired from CiteSeer. The outcomes revealed that most of the time the proposed approach outperformed other state-of-the-art techniques. The average correlation of the proposed approach is increased by 68 % as compared to the co-citation-based approach. In comparison with CPA approach, the average correlation of the proposed approach is increased by 39 % with respect to gold-standard rankings.

Scientific papers hold an association with previous research contributions (i.e. books, journals or conference papers, and web resources) in the form of citations. Citations are deemed as a link or relatedness of the previous work to the cited work. The nature of the cited material could be supportive (positive), contrastive (negative), or objective (neutral). Extraction of the author's sentiment towards the cited scientific articles is an emerging research discipline due to various linguistic differences between the citation sentences and other domains of sentiment analysis. In this paper, we propose a technique for the identification of the sentiment of the citing author towards the cited paper by extracting unigram, bigram, trigram, and pentagram adjective and adverb patterns from the citation text. After POS tagging of the citation text, we use the sentence parser for the extraction of linguistic features comprising adjectives, adverbs, and n-grams from the citation text. A sentiment score is then assigned to distinguish them as positive, negative, and neutral. In addition, the proposed technique is compared with manually classified citation text and 2 commercial tools, namely SEMANTRIA and THEYSAY, to determine their applicability to the citation corpus. These tools are based on different techniques for determining the sentiment orientation of the sentence. Analysis of the results shows that our proposed approach has achieved results comparable to the commercial counterparts with average precision, recall, and accuracy of 90{\%}, 81.82{\%}, and 85.91{\%} respectively.