Abstract
Business news helps leaders and entrepreneurs in decision-making every day. This involves making corporate strategies, taking marketing decisions, planning operations, investing human capital, etc. This news gives a complete idea to leaders and entrepreneurs about what is happening in the corporate world. They maintain track of all mergers and takeovers and make interested people informed. Today, it is essential for people to keep themselves updated about corporate business. As there are so many news websites and the same news article gets published on each of these websites with a little changed title. As a consequence of which people have to spend far longer trying to find information than the time they have to catch up on the news. So, it would be very helpful for such people, if clusters of semantically similar news articles from different websites could be created, and reading only one news item from one cluster will be sufficient. This chapter will explain a few approaches to aggregate similar news articles. The very first step is to collect the data. Initially, for developing the model, data is collected from sites such as Kaggle, UCI, etc. After the model is developed, real-time data can be collected using the news API. The second step is to preprocess the collected data which involves subtasks such as Tokenization, Stop-Word Removal, Stemming/Lemmatization, Case Transformation, etc. The third step here is Embedding Text to Vectors, using various embedding techniques such as Bag-of-Words, TF-IDF, Word2Vec, etc. The next task here is to make clusters of these embedded vectors or numbers, using various unsupervised learning algorithms such as K-means, agglomerative, etc. Finally, the last step here is to find a comparison of various combinations of embedding techniques and clustering algorithms.
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Tarbani, N., Wadhva, K. (2022). Aggregation of Semantically Similar News Articles with the Help of Embedding Techniques and Unsupervised Machine Learning Algorithms: A Machine Learning Application with Semantic Technologies. In: Houssein, E.H., Abd Elaziz, M., Oliva, D., Abualigah, L. (eds) Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems. Studies in Computational Intelligence, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-99079-4_5
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