A Contrast Between Bert and Word2vec's Approaches to Text Sentiment Analysis
- By Manish Nagpal1
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View Affiliations Hide Affiliations1 Centre for Interdisciplinary Research in Business and Technology, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
- Source: Demystifying Emerging Trends in Machine Learning , pp 316-326
- Publication Date: February 2025
- Language: English
A Contrast Between Bert and Word2vec's Approaches to Text Sentiment Analysis, Page 1 of 1
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A novel approach to creating a dataset to train a neural network that can analyze the tone of social media postings is proposed in this study. The paper goes on to detail how the word2vec and BERT algorithms may be used in a neural network to analyze social media messages and evaluate their emotional tone. A hybrid of cosine similarity as well as ontological mappings based on a tweaked version of the Term Frequency-Inverse Document Frequency (TFIDF) characteristics is used by the algorithm for semantic searching. The execution includes sentiment analysis, phrase extraction process, textual belief indicator, keyword-based search, as well as text summary, among other things. Additionally, trials were carried out proving the efficacy of the methods presented. The efficiency of stemming and lemmatization of the text in creating a training set for sentiment analysis was also tested experimentally.
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