USTORY

Libraries

• pandas 1.2.4
• sklearn 0.24.2
• numpy 1.19.5
• scipy 1.4.1
• sentence-transformers 2.0.0
• torch 1.8.2 (for sentence-transformer)
• spherical_kmeans (source)
• b3 (source)

Data sets

Raw data sets (before preprocessed)

• Data sets link: link
  • Newsfeed14 source
  • WCEP18/19 source
  • USNews (case study) - included in the link

Preprocessing

1. Download the raw data sets in the above link, or you can prepare your own data set ('.csv','.json',..) where the row format is ['title', 'date', 'text', 'id', 'story' (if available)]
2. Run Dataset_preprocessing.ipynb to preprocess the data set

USTORY usage

Parameters

• file_path: the path to a preprocessed data set file
• window_size: the size of window in desired time units (e.g., days) - default = 7
• slide_size: the size of slide in desired time units (e.g., days) - default = 1
• num_windows: the total number of windows to evaluate - default = 365
• min_articles: the minimum number of articles to form a story (e.g., 8 for Newsfeed14 and 18 for WCEP18/19 and USNews by default)
• N: the number of thematic keywords - default = 10
• T: the temperature for scaling the confidence score - default: 2
• keyword_score: the type of keyword score function in ["tfidf", "bm25"] - default = "tfidf"
• verbose: print the intermediate process in ["True", "False"] - default = "False"
• story_label: the existence of label for data set (to evaluate accuracy) in ["True", "False"] - default = "True"

Run an example simulation

Output items (in order)

(all_window, cluster_keywords_df, final_num_cluster, avg_win_proc_time, nmi, ami, ri, ari, precision, recall, fscore)

• all_window: include all article information and cluster assignment/confidence information
• cluster_keywords_df: the lists of thematic keywords of clusters in every window

from USTORY import *
output = simulate(file_path, window_size, slide_size, begin_date, num_windows, min_articles, N, T, keyword_score, verbose, story_label)
print(fscore: output[-1])