JOURNAL ARTICLE

Twitter Sentiment Analysis Using Deep Learning

NehaHritik GuptaSagar Dhanraj PandeAditya KhampariaVaishali BhagatNikhil E. Karale

Year: 2021 Journal:   IOP Conference Series Materials Science and Engineering Vol: 1022 (1)Pages: 012114-012114   Publisher: IOP Publishing

Abstract

Abstract It is well established that the tweet sentiment analysis is still focused on conventional messages, such as film reviews and product reviews, while significant improvement has been made as deep learning becomes widespread, and comprehensive data sets are accessible for training (far from just emoticons and hashtags). Nevertheless, prior opinion analysis experiments typically performed on tweets, i.e. only two forms of global polarities (i.e. optimistic and negative) occur with their work/validation/test data sets. What is more, systems’ judgments are not actively aligned with the specified appraisal objects. In this paper, we have discussed some deep learning approaches for twitter sentiment analysis. We also trained our model using CNN and RNN to get some good accuracy results.

Keywords:
Sentiment analysis Computer science Deep learning Artificial intelligence Product (mathematics) Data science Natural language processing Machine learning Information retrieval Mathematics

Metrics

13
Cited By
0.98
FWCI (Field Weighted Citation Impact)
15
Refs
0.79
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Sentiment Analysis and Opinion Mining
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Text Analysis Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence

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