Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Contextual semantics for sentiment analysis of Twitter
2015313 citationsHassan Saif, Yulan He et al.Information Processing & Managementprofile →
Citations per year, relative to Hassan Saif Hassan Saif (= 1×)
peers
Adam Bermingham
Countries citing papers authored by Hassan Saif
Since
Specialization
Citations
This map shows the geographic impact of Hassan Saif's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hassan Saif with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hassan Saif more than expected).
This network shows the impact of papers produced by Hassan Saif. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hassan Saif. The network helps show where Hassan Saif may publish in the future.
Co-authorship network of co-authors of Hassan Saif
This figure shows the co-authorship network connecting the top 25 collaborators of Hassan Saif.
A scholar is included among the top collaborators of Hassan Saif based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Hassan Saif. Hassan Saif is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Burel, Grégoire, Hassan Saif, Miriam Fernández, & Harith Alani. (2017). On Semantics and Deep Learning for Event Detection in Crisis Situations. Open Research Online (The Open University).30 indexed citations
Saif, Hassan, Miriam Fernández, Matthew Rowe, & Harith Alani. (2016). On the Role of Semantics for Detecting pro-ISIS Stances on Social Media.. Open Research Online (The Open University).4 indexed citations
5.
Uren, Victoria, Daniel G. Wright, James A. Scott, Yulan He, & Hassan Saif. (2016). Social media and sentiment in bioenergy consultation. International Journal of Energy Sector Management. 10(1). 87–98.6 indexed citations
6.
Saif, Hassan, Yulan He, Miriam Fernández, & Harith Alani. (2015). Contextual semantics for sentiment analysis of Twitter. Information Processing & Management. 52(1). 5–19.313 indexed citations breakdown →
7.
Saif, Hassan, Miriam Fernández, Yulan He, & Harith Alani. (2014). On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter. Language Resources and Evaluation. 810–817.133 indexed citations
Saif, Hassan, Miriam Fernández, Yulan He, & Harith Alani. (2013). Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold.. Open Research Online (The Open University). 9–21.146 indexed citations
10.
He, Yulan, Hassan Saif, Zhongyu Wei, & Kam‐Fai Wong. (2012). Quantising Opinions for Political Tweets Analysis. Language Resources and Evaluation. 3901–3906.18 indexed citations
11.
Saif, Hassan, Yulan He, & Harith Alani. (2012). Alleviating Data Sparsity for Twitter Sentiment Analysis. Open Research Online (The Open University). 2–9.105 indexed citations
Saif, Hassan, Yulan He, & Harith Alani. (2011). Semantic smoothing for Twitter sentiment analysis. Open Research Online (The Open University).16 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.