# Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model

arXiv: https://arxiv.org/abs/2404.17123 · HTML: https://mlsanity.com/paper/2404.17123/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation](https://mlsanity.com/paper/2208.10531/) (2022)

## Built on by (most cited first, 10 of 10)

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- [Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness](https://mlsanity.com/paper/2408.04585/) (2024)
- [TD3 Based Collision Free Motion Planning for Robot Navigation](https://mlsanity.com/paper/2405.15460/) (2024)
- [Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis](https://mlsanity.com/paper/2407.16150/) (2024)
- [Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising](https://mlsanity.com/paper/2406.10239/) (2024)
- [Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding](https://mlsanity.com/paper/2405.13381/) (2024)
- [Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integrated](https://mlsanity.com/paper/2406.04658/) (2024)
- [CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction](https://mlsanity.com/paper/2409.13701/) (2024)
- [Backdoor Learning in Language Models and Vision-Language Models](https://mlsanity.com/paper/2608.18095/) (2026)

## Abstract (arXiv)

This paper explores the importance of text sentiment analysis and classification in the field of natural language processing, and proposes a new approach to sentiment analysis and classification based on the bidirectional gated recurrent units (GRUs) model. The study firstly analyses the word cloud model of the text with six sentiment labels, and then carries out data preprocessing, including the steps of removing special symbols, punctuation marks, numbers, stop words and non-alphabetic parts. Subsequently, the data set is divided into training set and test set, and through model training and testing, it is found that the accuracy of the validation set is increased from 85% to 93% with training, which is an increase of 8%; at the same time, the loss value of the validation set decreases from 0.7 to 0.1 and tends to be stable, and the model is gradually close to the actual value, which can effectively classify the text emotions. The confusion matrix shows that the accuracy of the model on the test set reaches 94.8%, the precision is 95.9%, the recall is 99.1%, and the F1 score is 97.4%, which proves that the model has good generalisation ability and classification effect. Overall, the study demonstrated an effective method for text sentiment analysis and classification with satisfactory results.