Reddit sentiment analysis for natural language processing

Research Article
Open access

Reddit sentiment analysis for natural language processing

Ang li 1*
  • 1 Hanyang University, Department of Computer Science, 15588, Seoul, South Korea    
  • *corresponding author angli96116@gmail.com
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

In the Internet age, social media has fully penetrated into people's lives. As one of the well-developed online platforms with a large user base, Reddit allows users to independently publish current news, life experiences, and interesting life stories. However, sometimes it sends a negative tone that affects the brand of a company or individual and destroys profits and it is necessary to prevent Twitter by identifying hate words. The biggest innovation of this post is that we use reddit data to compare various methods simultaneously. As we process more data, trying deep learning will yield good results. Compared to other machine learning classifiers, the transformer classifier achieves the best results.

Keywords:

Transformer Classifier, NLP, VADER, Sentiment Analysis, API, Python, Polarity, Evaluation Metrics Introduction.

li,A. (2023). Reddit sentiment analysis for natural language processing. Applied and Computational Engineering,5,579-582.
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References

[1]. Pang B, Lee L.Opinion Mining and Sentiment Analysis[j].Foundations and trends in Information Retrieval,2008,2(1-2):1-135Pang B, Lee L.Opinion Mining and Sentiment Analysis[j].Foundations and trends in Information Retrieval,2008,2(1-2):1-135

[2]. Idicula-Thomas S,Kulkarni, A J,Kulkarni B D,et al.A Support Vector Machine-based Method for Predicting the Propensity of a Protein to be Soluble or to Form Inclusion Body on Overexpression in Escherichia Coli[J].Bioinformatics, 2006(22):278-284.

[3]. Alharbi, A.S.M.; de Doncker, E. Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information. Cogn. Syst. Res. 2019, 54, 50–61. [CrossRef]

[4]. Abid, F.; Alam, M.; Yasir, M.; Li, C.J. Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter. Future Gener. Comput. Syst. 2019, 95, 292–308. [CrossRef]

[5]. Pouli, V.; Kafetzoglou, S.; Tsiropoulou, E.E.; Dimitriou, A.; Papavassiliou, S. Personalized multimedia content retrieval through relevance feedback techniques for enhanced user experience. In Proceedings of the 2015 13th International Conference on Telecommunications (ConTEL), Graz, Austria, 13–15 July 2015; pp. 1–8.

[6]. Kraus, M.; Feuerriegel, S. Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees. Expert Syst. Appl. 2019, 118, 65–79. [CrossRef]

Cite this article

li,A. (2023). Reddit sentiment analysis for natural language processing. Applied and Computational Engineering,5,579-582.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Pang B, Lee L.Opinion Mining and Sentiment Analysis[j].Foundations and trends in Information Retrieval,2008,2(1-2):1-135Pang B, Lee L.Opinion Mining and Sentiment Analysis[j].Foundations and trends in Information Retrieval,2008,2(1-2):1-135

[2]. Idicula-Thomas S,Kulkarni, A J,Kulkarni B D,et al.A Support Vector Machine-based Method for Predicting the Propensity of a Protein to be Soluble or to Form Inclusion Body on Overexpression in Escherichia Coli[J].Bioinformatics, 2006(22):278-284.

[3]. Alharbi, A.S.M.; de Doncker, E. Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information. Cogn. Syst. Res. 2019, 54, 50–61. [CrossRef]

[4]. Abid, F.; Alam, M.; Yasir, M.; Li, C.J. Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter. Future Gener. Comput. Syst. 2019, 95, 292–308. [CrossRef]

[5]. Pouli, V.; Kafetzoglou, S.; Tsiropoulou, E.E.; Dimitriou, A.; Papavassiliou, S. Personalized multimedia content retrieval through relevance feedback techniques for enhanced user experience. In Proceedings of the 2015 13th International Conference on Telecommunications (ConTEL), Graz, Austria, 13–15 July 2015; pp. 1–8.

[6]. Kraus, M.; Feuerriegel, S. Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees. Expert Syst. Appl. 2019, 118, 65–79. [CrossRef]

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