August 11, 2025International Journal of Innovative Research and Scientific Studies0 citations

Hybrid Model for Automatic Document Classification Using Vectorization and Machine Learning

Document analysis via combined vectorization and machine learning approaches

View Full Paper

Ask AI

Bookmark

Authors

DKDinara Kaibassova BMBigul MUKHAMETZHANOVA DTDinara Tokseit

Discussion

Member takes

Overview

The study demonstrates a significant improvement in document classification with a hybrid approach combining TF-IDF and Word2Vec, highlighting implications for various fields.

Key Points

The Word2Vec + SVM model achieved 90.2% accuracy in document classification, outperforming other configurations.
Incorporating statistical and semantic vectorization techniques improves the precision and generalizability of models.
A comprehensive approach involving data preprocessing and feature extraction is crucial for effective machine learning applications.
Practical applications of the developed model include text classification, sentiment analysis, and topic modeling in healthcare and legal domains.

Cite This Study

Kaibassova et al. (2025) studied this question.

synapsesocial.com/papers/68a360d60a429f79733290ca https://doi.org/10.53894/ijirss.v8i4.8356

View Full Paper

Ask AI

Bookmark

August 11, 2025International Journal of Innovative Research and Scientific Studies0 citations

Hybrid Model for Automatic Document Classification Using Vectorization and Machine Learning

Document analysis via combined vectorization and machine learning approaches

View Full Paper

Ask AI

Bookmark

Authors

DKDinara Kaibassova BMBigul MUKHAMETZHANOVA DTDinara Tokseit

Discussion

Member takes

Overview

The study demonstrates a significant improvement in document classification with a hybrid approach combining TF-IDF and Word2Vec, highlighting implications for various fields.

Key Points

The Word2Vec + SVM model achieved 90.2% accuracy in document classification, outperforming other configurations.
Incorporating statistical and semantic vectorization techniques improves the precision and generalizability of models.
A comprehensive approach involving data preprocessing and feature extraction is crucial for effective machine learning applications.
Practical applications of the developed model include text classification, sentiment analysis, and topic modeling in healthcare and legal domains.

Cite This Study

Kaibassova et al. (2025) studied this question.

synapsesocial.com/papers/68a360d60a429f79733290ca https://doi.org/10.53894/ijirss.v8i4.8356

View Full Paper

Ask AI

Bookmark