Feature Enhancement Based Text Sentiment Classification using Deep Learning Model

被引：0

作者：

Janardhana, D. R. ^{[1
]}

Vijay, C. P. ^{[2
]}

Swamy, G. B. Janardhana ^{[1
]}

Ganaraj, K. ^{[1
]}

机构：

[1] Sahyadri Coll Engn & Management, Dept Informat Sci Engn, Managaluru, India

[2] Vidyavardhaka Coll Engn, Dept Informat Sci Engn, Mysore, Karnataka, India

来源：

PROCEEDINGS OF THE 2020 5TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND SECURITY (ICCCS-2020) | 2020年

关键词：

Text sentiment; Convolutional Neural Networks (CNN); Recurrent Neural Networks (RNN); Long Short-term Memory (LSTM); Convolutional Recurrent Neural Networks (CRNN);

D O I：

10.1109/icccs49678.2020.9277109

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Text sentiment classification is a significant task in the recent years to understand the opinions and thoughts hidden in the text to enhance more productivity in e-commerce websites and also in the social media. Here we integrate deep learning models to analyze the text sentiments. In this paper, Convolutional Recurrent Neural Network (CRNN) method for text sentiment analysis is proposed. The proposed CRNN is a combination of different layers used to extract the features from the text dataset. During training CRNN is able to learn the features set of the text sentiment dataset. The performance of the proposed approach is evaluated on text sentiments of publically available movie review (MR) dataset. Results show that the proposed method outperforms the traditional deep learning techniques

引用

页数：6

共 50 条

[1] Text Sentiment Classification Algorithm Based on Prompt Learning Enhancement
Taifeng, Huang
Jing, Ma
[J]. Data Analysis and Knowledge Discovery, 2024, 8 (03) : 77 - 84
[2] Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion
Abdi, Asad
Shamsuddin, Siti Mariyam
Hasan, Shafaatunnur
Piran, Jalil
[J]. INFORMATION PROCESSING & MANAGEMENT, 2019, 56 (04) : 1245 - 1259
[3] A Hybrid Method for Bilingual Text Sentiment Classification Based on Deep Learning
Liu, Guolong
Xu, Xiaofei
Deng, Bailong
Chen, Siding
Li, Li
[J]. 2016 17TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD), 2016, : 93 - 98
[4] A study into text sentiment analysis model based on deep learning
Luo, Zhubin
[J]. International Journal of Information and Communication Technology, 2024, 24 (08) : 64 - 75
[5] Sentiment classification: Feature selection based approaches versus deep learning
Uysal, Alper Kursat
Murphey, Yi Lu
[J]. 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (CIT), 2017, : 23 - 30
[6] Chinese Text Feature Extraction and Classification Based on Deep Learning
Wang, Ruishuang
Li, Zhao
Cao, Jian
Chen, Tong
[J]. PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE2019), 2019,
[7] A feature selection model based on genetic rank aggregation for text sentiment classification
Onan, Aytug
Korukoglu, Serdar
[J]. JOURNAL OF INFORMATION SCIENCE, 2017, 43 (01) : 25 - 38
[8] Morphological evaluation and sentiment analysis of Punjabi text using deep learning classification
Singh, Jaspreet
Singh, Gurvinder
Singh, Rajinder
Singh, Prithvipal
[J]. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2021, 33 (05) : 508 - 517
[9] CLASSIFICATION OF SENTIMENT USING OPTIMIZED HYBRID DEEP LEARNING MODEL
Touate, Chaima Ahle
EL Ayachi, Rachid
Biniz, Mohamed
[J]. COMPUTING AND INFORMATICS, 2023, 42 (03) : 651 - 666
[10] Sentiment Classification Using fastText Embedding and Deep Learning Model
Khasanah, Isnaini Nurul
[J]. AI IN COMPUTATIONAL LINGUISTICS, 2021, 189 : 343 - 350

← 1 2 3 4 5 →