Research Output
A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments
  Stance detection is a relatively new concept in data mining that aims to assign a stance label (favor, against, or none) to a social media post towards a specific pre-determined target. These targets may not be referred to in the post, and may not be the target of opinion in the post. In this paper, we propose a novel enhanced method for identifying the writer’s stance of a given tweet. This comprises a three-phase process for stance detection: (a) tweets preprocessing; here we clean and normalize tweets (e.g., remove stop-words) to generate words and stems lists, (b) features generation; in this step, we create and fuse two dictionaries for generating features vector, and lastly (c) classification; all the instances of the features are classified based on the list of targets. Our innovative feature selection proposes fusion of two ranked lists (top-) of term frequency-inverse document frequency (tf-idf) scores and the sentiment information. We evaluate our method using six different classifiers: nearest neighbor (K-NN), discernibility-based K-NN, weighted K-NN, class-based K-NN, exemplar-based K-NN, and Support Vector Machines. Furthermore, we investigate the use of Principal Component Analysis and study its effect on performance. The model is evaluated on the benchmark dataset (SemEval-2016 task 6), and the results significance is determined using t-test. We achieve our best performance of macro -score (averaged across all topics) of 76.45% using the weighted K-NN classifier. This tops the current state-of-the-art score of 74.44% on the same dataset.

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  • Date:

    15 October 2020

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  • Publisher

    Elsevier BV

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  • Funders:

    Deanship of Scientific Research, King Saud University; Engineering and Physical Sciences Research Council


Al-Ghadir, A. I., Azmi, A. M., & Hussain, A. (2021). A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments. Information Fusion, 67, 29-40.



Stance detection, Sentiment analysis, Top-k, K-NN variants, Support vector machines, Twitter

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