An Automated Model for Sentimental Analysis Using Long Short-Term Memory-based Deep Learning Model

Full Text (PDF, 503KB), PP.11-20

Views: 0 Downloads: 0


Shashank Mishra 1,* Mukul Aggarwal 1 Shivam Yadav 1 Yashika Sharma 1

1. KIET Group of Institutions, Uttar Pradesh, Delhi NCR, Ghaziabad, India

* Corresponding author.


Received: 18 Apr. 2023 / Revised: 9 Jun. 2023 / Accepted: 7 Jul. 2023 / Published: 8 Oct. 2023

Index Terms

Tokenizers, LSTM Model, Sentiment, NLP, Machine Learning, Binary Text Classification


A post, review, or news article's emotional tone can be automatically ascertained using sentiment analysis, a natural language processing approach. Sorting the text into positive, negative, or neutral categories is the aim of sentiment analysis. Many methods, including rule-based systems and machine learning algorithms, can be used to analyse sentiment, or deep learning models. These techniques typically involve analyzing various features of the text, such as word choice, sentence structure, and context, to identify the overall sentiment. Here long short-term memory-based deep learning is applied in this research for the model development purpose. Deeply interconnected neural networks are used in this method. Sentiment analysis can be used in many different applications, such as market research, brand reputation management, customer feedback analysis, and social media monitoring. It shows the use of sentiment analysis in a variety of fields and increases the need of technology to perform it on the existing machines.

Cite This Paper

Shashank Mishra, Mukul Aggarwal, Shivam Yadav, Yashika Sharma, "An Automated Model for Sentimental Analysis Using Long Short-Term Memory-based Deep Learning Model", International Journal of Engineering and Manufacturing (IJEM), Vol.13, No.5, pp. 11-20, 2023. DOI:10.5815/ijem.2023.05.02


[1]S. Mishra, M. Aggarwal, S. Yadav and Y. Sharma, "Comparison of Machine Learning Techniques for Sentiment Analysis," 2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), Kalady, Ernakulam, India, 2023, pp. 184-191, doi: 10.1109/ACCESS57397.2023.10200806.
[2]Igor Mozeti , Miha GrĖ‡car , Jasmina Smailovi , “Multilingual Twitter Sentiment Classification: The Role of Human Annotators” , 5 May 2016.
[3]Yili Wang , Jiaxuan Guo , Chengsheng Yuan and Baozhu Li , “Sentiment Analysis of Twitter Data” , 19 November 2022
[5]Hamid Bagheri , Md Johirul Islam , “Sentiment analysis of twitter data”.
[6]Apoorv Agarwal, Boyi Xie , Ilia Vovsha, Owen Rambow , Rebecca Passonneau , “Sentiment Analysis of Twitter Data”.
[7]VANAMA YASWANTH , VIDHI MATHUR , SNEH SINGH, “Sentiments Analysis Using Tweets” , 31 May 2022.
[8]Devi Ajeng Efrilianda , Erika Noor Dianti , Oktaria Gina Khoirunnisa, “Analysis of twitter sentiment in COVID-19 era using fuzzy logic method” , 12 March 2021.
[9]Vishal A. Kharde , S.S. Sona “Sentiment Analysis of Twitter Data: A Survey of Techniques” , 11 April 2016
[10]Dr.Jyothi Mandala,Pragada Akhila, Uriti Archana , “An Exploration of Sentiment Analysis using Twitter Dataset” , 18 November 2020.
[11]Dwivedi, R.K., Aggarwal, M., Keshari, S.K., Kumar, A. (2019). Sentiment Analysis and Feature Extraction Using Rule-Based Model (RBM). In: Bhattacharyya, S., Hassanien, A., Gupta, D., Khanna, A., Pan, I. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 56. Springer, Singapore.
[12]Md. Rakibul Hasan , Maisha Maliha , M. Arifuzzaman , “Sentiment Analysis with NLP on Twitter Data” ,July 2019.
[13]Anupama B , Rakshith D B, Rahul Kumar M, Navaneeth M , “Real Time Twitter Sentiment Analysis using Natural Language Processing” , 7 July 2020.
[14]Abdullah Alsaeedi , Mohammad Zubair Khan , “A Study on Sentiment Analysis Techniques of Twitter Data” 2019.
[15]K. Arun , A. Srinagesh , “Multi-lingual Twitter sentiment analysis using machine learning” , 6 December 2020.
[16]Saurabh Singh , “Twitter Sentiments Analysis Using Machine Learning” , 27 July 2020.
[17]'%20LSTM%20stands%20for%20long%20short,especially%20in%20sequence%20predict ion%20problems
[18]Dr. Pamela Vinitha Eric, Anu Priya K R , “ Twitter Sentimental Analysis using Deep Learning Techniques” , 5 August 2020.
[19]Dr. P. Sumathy , S. M. Muthukumari , “Sentiment Analysis of Twitter Data Using Multi Class Semantic Approach” , 24 July 2018.
[20]Ankita Sharma, Udayan Ghose , “Sentimental Analysis of Twitter Data with respect to General Elections in India” , 2020
[21]Md Ashique , Satyam Kumar , Aanchal Vij , Swapnil Panwar , “Sentiment Analysis Using machines Learning Approaches of Twitter Data and Semantic Analysis” , 2021.
[22]Pratima Deshpande , Purva Joshi , Diptee Madekar , Pratiksha Pawar , Prof. M.D. Salunke ,” A Survey On: Classification of Twitter data Using Sentiment Analysis”.