Abstract
Incorporating semantic information into any similarity metric increases its effectiveness and yields findings that may be further analyzed using human interpretation. There will be fewer accurate findings if the similarity is calculated based only on the text’s words. Three alternative approaches are shown in this study, each of which uses a feature vector that combines semantic information from readers and calculates similarities between them. These methods—LSA using word2vec, Explicit Semantic Analysis using Bag-of-Words, and Soft Cosine Similarity using TF-IDF—are based on textual data and knowledge-based methodologies. The technique produces simple-to-read documents that can be used in different information retrieval systems. When comparing commonalities between brief news texts, Latent Semantic Analysis employing Word2Vec Vectors outperformed the other two.
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Srinivasarao, U., Karthikeyan, R., Bilal, M.J., Hariharan, S. (2023). Comparison of Different Similarity Methods for Text Categorization. In: Bhattacharya, A., Dutta, S., Dutta, P., Piuri, V. (eds) Innovations in Data Analytics. ICIDA 2022. Advances in Intelligent Systems and Computing, vol 1442. Springer, Singapore. https://doi.org/10.1007/978-981-99-0550-8_39
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