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  1. 21
    Schematic of the approach: This schematic illustrates the entire workflow of the project.

    Schematic of the approach: This schematic illustrates the entire workflow of the project. by Enock Niyonkuru (21246879)

    Published 2025
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  2. 22
    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739).

    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). by Enock Niyonkuru (21246879)

    Published 2025
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  3. 23
    Comparative analysis of WordNet replacement impact on data distribution.

    Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

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  4. 24
    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10, this Table shows the results for three different window sizes at a =. Abbreviations are the same as for Table 1.

    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... by Enock Niyonkuru (21246879)

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  5. 25
    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet.

    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet. by Enock Niyonkuru (21246879)

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  6. 26
    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,918. The Table summarizes results for different thresholds () and categories of concept/gene sets (M,B,K,G,P). Columns: : Replacement threshold; replaced: Unique Replaced Concepts; Category: M = MeSH, B = Biocarta, K = KEGG, G = GP(bp), P = PID; # sets: Number of concept/gene sets in the categories; #Concepts: number of concept vectors in the category; <i>WN better</i><i>: The count and percentage of concept/gene sets for which the mean interconcept distance was smaller for WN than for PM. “Winners” are shown in bold.;</i> <i>PM better</i><i>: Analogous to “WN better” but for PM.</i>

    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... by Enock Niyonkuru (21246879)

    Published 2025
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  7. 27
    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of concept sets where the cluster mean distance is significantly lower, indicative of superior embeddings.

    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... by Enock Niyonkuru (21246879)

    Published 2025
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  8. 28
    This package of Python scripts implements the proposed unsupervised sentiment analysis approach. It contains a pre-processing script (preprocess.py), a script that calculates BERT embeddings (bert_encoder.py), a script to enrich embeddings with sentiment (enrich_embeddings.py), a script to build the graph (build_graph.py), a script to compute a graph attention module (gat_module.py), a script to do clustering and evaluation (clustering_and_evaluation.py), and utility/evaluation scripts. These files permit a complete replication of the experiments.

    This package of Python scripts implements the proposed unsupervised sentiment analysis approach. It contains a pre-processing script (preprocess.py), a script that calculates BERT... by Xiaohan Yu (7525940)

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  9. 29
    Number of tweets collected over time.

    Number of tweets collected over time. by Sylvia Iasulaitis (8301189)

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  10. 30
    Descriptive measures of the dataset.

    Descriptive measures of the dataset. by Sylvia Iasulaitis (8301189)

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  11. 31
    Corpora from the articles in order of size.

    Corpora from the articles in order of size. by Sylvia Iasulaitis (8301189)

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  12. 32
    Media information.

    Media information. by Sylvia Iasulaitis (8301189)

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  13. 33
    Information from interactions.

    Information from interactions. by Sylvia Iasulaitis (8301189)

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  14. 34
    Table of the database statistical measures.

    Table of the database statistical measures. by Sylvia Iasulaitis (8301189)

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  15. 35
    Tweets information.

    Tweets information. by Sylvia Iasulaitis (8301189)

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  16. 36
    Examples of tweets texts (Portuguese).

    Examples of tweets texts (Portuguese). by Sylvia Iasulaitis (8301189)

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  17. 37
    Methodological flowchart.

    Methodological flowchart. by Sylvia Iasulaitis (8301189)

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  18. 38
    Number of tweets collected per query and type.

    Number of tweets collected per query and type. by Sylvia Iasulaitis (8301189)

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  19. 39
    Examples of tweets texts (English).

    Examples of tweets texts (English). by Sylvia Iasulaitis (8301189)

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  20. 40
    Users information.

    Users information. by Sylvia Iasulaitis (8301189)

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