Details

Autor(en) / Beteiligte

• Srinivas, Akana Chandra Mouli Venkata
• Satyanarayana, Ch
• Divakar, Ch
• Sirisha, Katikireddy Phani

Titel

Sentiment Analysis using Neural Network and LSTM

Ist Teil von

• IOP conference series. Materials Science and Engineering, 2021-02, Vol.1074 (1), p.12007

Ort / Verlag

Bristol: IOP Publishing

Erscheinungsjahr

2021

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Abstract People put their opinions or views on various events happening in the society or world. Twitter is one of the best social networking sites where a huge amount of data generates on the daily basis. These data can be used to classify their tweets based on various sentiments attached to them. Numerous technologies are applied to analyse the sentiments of users. Sentiment analysis needs a very efficient method to manage long arrangement data and their drawn-out dependencies. In this paper, we have applied a deep learning technique to perform Twitter sentiment analysis. Simple Neural Network, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) methods are applied for the sentiment analysis and their performances are evaluated. The LSTM is the best among all proposed techniques with the highest accuracy of 87%. We have collected a Twitter dataset from Kaggle to perform our experiment. The future improvement of the proposed research should include REST APIs and web crawling-based solutions to get live tweets to perform real-time analytics. We have analysed 1.6 million tweets in our research work.