Browsing Research Institute in Information and Language Processing by Authors
Exploiting tweet sentiments in altmetrics large-scale dataHassan, Saeed-Ul; Aljohani, Naif Radi; Iqbal Tarar, Usman; Safder, Iqra; Sarwar, Raheem; Alelyani, Salem; Nawaz, Raheel (SAGE, 2022-12-31)This article aims to exploit social exchanges on scientific literature, specifically tweets, to analyse social media users' sentiments towards publications within a research field. First, we employ the SentiStrength tool, extended with newly created lexicon terms, to classify the sentiments of 6,482,260 tweets associated with 1,083,535 publications provided by Altmetric.com. Then, we propose harmonic means-based statistical measures to generate a specialized lexicon, using positive and negative sentiment scores and frequency metrics. Next, we adopt a novel article-level summarization approach to domain-level sentiment analysis to gauge the opinion of social media users on Twitter about the scientific literature. Last, we propose and employ an aspect-based analytical approach to mine users' expressions relating to various aspects of the article, such as tweets on its title, abstract, methodology, conclusion, or results section. We show that research communities exhibit dissimilar sentiments towards their respective fields. The analysis of the field-wise distribution of article aspects shows that in Medicine, Economics, Business & Decision Sciences, tweet aspects are focused on the results section. In contrast, Physics & Astronomy, Materials Sciences, and Computer Science these aspects are focused on the methodology section. Overall, the study helps us to understand the sentiments of online social exchanges of the scientific community on scientific literature. Specifically, such a fine-grained analysis may help research communities in improving their social media exchanges about the scientific articles to disseminate their scientific findings effectively and to further increase their societal impact.
Parsing AUC result-figures in machine learning specific scholarly documents for semantically-enriched summarizationSafder, Iqra; Batool, Hafsa; Sarwar, Raheem; Zaman, Farooq; Aljohani, Naif Radi; Nawaz, Raheel; Gaber, Mohamed; Hassan, Saeed-Ul (Taylor & Francis, 2021-11-14)Machine learning specific scholarly full-text documents contain a number of result-figures expressing valuable data, including experimental results, evaluations, and cross-model comparisons. The scholarly search system often overlooks this vital information while indexing important terms using conventional text-based content extraction approaches. In this paper, we propose creating semantically enriched document summaries by extracting meaningful data from the results-figures specific to the evaluation metric of the area under the curve (AUC) and their associated captions from full-text documents. At first, classify the extracted figures and analyze them by parsing the figure text, legends, and data plots – using a convolutional neural network classification model with a pre-trained ResNet-50 on 1.2 million Images from ImageNet. Next, we extract information from the result figures specific to AUC by approximating the region under the function's graph as a trapezoid and calculating its area, i.e., the trapezoidal rule. Using over 12,000 figures extracted from 1000 scholarly documents, we show that figure specialized summaries contain more enriched terms about figure semantics. Furthermore, we empirically show that the trapezoidal rule can calculate the area under the curve by dividing the curve into multiple intervals. Finally, we measure the quality of specialized summaries using ROUGE, Edit distance, and Jaccard Similarity metrics. Overall, we observed that figure specialized summaries are more comprehensive and semantically enriched. The applications of our research are enormous, including improved document searching, figure searching, and figure focused plagiarism. The data and code used in this paper can be accessed at the following URL: https://github.com/slab-itu/fig-ir/.
Webometrics: evolution of social media presence of universitiesSarwar, Raheem; Zia, Afifa; Nawaz, Raheel; Fayoumi, Ayman; Aljohani, Naif Radi; Hassan, Saeed-Ul (Springer Science and Business Media LLC, 2021-01-03)This paper aims at an important task of computing the webometrics university ranking and investigating if there exists a correlation between webometrics university ranking and the rankings provided by the world prominent university rankers such as QS world university ranking, for the time period of 2005–2016. However, the webometrics portal provides the required data for the recent years only, starting from 2012, which is insufficient for such an investigation. The rest of the required data can be obtained from the internet archive. However, the existing data extraction tools are incapable of extracting the required data from internet archive, due to unusual link structure that consists of web archive link, year, date, and target links. We developed an internet archive scrapper and extract the required data, for the time period of 2012–2016. After extracting the data, the webometrics indicators were quantified, and the universities were ranked accordingly. We used correlation coefficient to identify the relationship between webometrics university ranking computed by us and the original webometrics university ranking, using the spearman and pearson correlation measures. Our findings indicate a strong correlation between ours and the webometrics university rankings, which proves that the applied methodology can be used to compute the webometrics university ranking of those years for which the ranking is not available, i.e., from 2005 to 2011. We compute the webometrics ranking of the top 30 universities of North America, Europe and Asia for the time period of 2005–2016. Our findings indicate a positive correlation for North American and European universities, but weak correlation for Asian universities. This can be explained by the fact that Asian universities did not pay much attention to their websites as compared to the North American and European universities. The overall results reveal the fact that North American and European universities are higher in rank as compared to Asian universities. To the best of our knowledge, such an investigation has been executed for the very first time by us and no recorded work resembling this has been done before.