Comparative Analysis of CNN, RNN and HAN for Text Classification with GloVe Data Model

To extract and sentiment analysis from a verbal description, text-based sentiment detection is employed. Text-based sentiment detection categorized into two main phases, including language representation and classification. Language representation proposes a robust technique to extract the contextual 2 information from the text to increase the quality of feature extraction. Classification employed neural networks to increase classification performance. These techniques have been applied to extract sentiment from the “IMDB” movie review dataset. Three general approaches are represented to detect sentiment analysis, including Rule Construction, Machine Learning (ML), and Hybrid Approaches. Outcome: provided the text processing techniques used in NLP and different feature extraction methods, including Bag of words, TF-IDF, Word2Vec, and Glove. Demonstrated the use of text processing and build a Sentiment Analyzer with classical ML approaches that achieved fairly good results. Described in detail the architecture of the Deep Learning model for sentiment classification. Hence, trained a word2vec model and used it as a pre-trained embedding for sentiment classification. This knowledge applied to experiment with deep learning NLP models to classify film reviews as positive or negative. Some of these models involved layer types (dense and convolutional layers), while later ones involved new layer types from the RNN family (LSTMs and GRUs). In a conclusion, deep learning models offer clear comprehensibility of the extracted feature prior to classification.

Text Classification Using Embedding and LSTM Recurrent Neural Network

This project focuses on implementing a deep learning system for text classification, specifically aimed at detecting hate speech and offensive language on social media platforms. It leverages context-independent word embedding techniques, such as word2vec, GloVe, and FastText, in conjunction with BiLSTM, a variant of RNN models.

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Comparative Analysis of CNN, RNN and HAN for Text Classification with GloVe Data

Sequence Modeling and Sentiment Analysis using RNNs and LSTMs. Implementation of text classification with pre-trained word embeddings (Word2Vec/GloVe).

A comprehensive study on multi-class text classification comparing traditional representations (BoW, TF-IDF) and word embeddings (Word2Vec, GloVe) across machine learning and deep learning models, including RNN-based architectures, with detailed performance analysis.

– Compared word representations (BoW, TF-IDF, GloVe, Skip-gram) with ML models (Logistic Regression, Naive Bayes, Random Forest) and NN models (DNN, RNN, GRU, Bidirectional LSTM) for multi-class text classification. – Used 340,000 question-answer pairs across ten categories.