Manuscript Number : IJSRST52211267

Enhancing Investment Strategies with NLP-Driven Sentiment Analysis from Social Media and News Sources

Authors(1) :-Paril Ghori

In recent years, Natural Language Processing (NLP) has emerged as a powerful tool in extracting meaningful insights from large volumes of unstructured data. This research explores the application of sentiment analysis, an NLP technique, to the field of investment decision-making. By analyzing user opinions and news articles related to specific investment assets, this study proposes a tool that helps investors gauge market sentiment in real-time. The core functionality of the application relies on gathering and processing data from social media platforms like Twitter and various news websites through web scraping techniques. Once the data is collected, it undergoes a series of pre-processing steps, including tokenization, normalization, and removal of stopwords, followed by sentiment classification using algorithms like TextBlob. The system generates visual representations, such as bar charts, word clouds, and trend graphs, to provide investors with a consolidated view of market sentiment. The results of the sentiment analysis are then summarized in a comprehensive report, enabling users to make more informed investment choices. The research demonstrates that by leveraging NLP, investors can gain a macro perspective on market dynamics, reducing information overload and making more data-driven decisions. Future enhancements of the tool may include incorporating additional investment types, refining sentiment analysis models, and expanding the scope of the web crawler to gather data from a wider range of financial news sources.

Authors and Affiliations

Paril Ghori

Investment Decision-making, Natural Language Processing (NLP), Sentiment Analysis, Social Media, TextBlob, Text Mining, Twitter API, Web Crawler, Word Cloud.

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Publication Details

Published in : Volume 9 | Issue 5 | September-October 2022
Date of Publication : 2022-09-15
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 738-752
Manuscript Number : IJSRST52211267
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Paril Ghori, " Enhancing Investment Strategies with NLP-Driven Sentiment Analysis from Social Media and News Sources", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 9, Issue 5, pp.738-752, September-October-2022.
Journal URL : https://ijsrst.com/IJSRST52211267
Citation Detection and Elimination     |      | |
  • Bollen, J., Mao, H. and Zeng, X., 2011. Twitter mood predicts the stock market. Journal of computational science, 2(1), pp.1-8.
  • Araújo, M., Gonçalves, P., & Benevenuto, F. (2013). "Comparing Sentiment Analysis Methods on Twitter." Proceedings of the International Conference on Web Intelligence, 157-164.
  • Ramos, P., & Benevenuto, F. (2016). "Sentiment Analysis on Twitter: A Comparison of Methods." Social Media and Society, 2(3), 78-90.
  • Araci, D., 2019. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. arXiv preprint arXiv:1908.10063.
  • Araci, D., 2019. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. arXiv preprint arXiv:1908.10063.
  • Ramagundam, S. (2022). Ai-Driven Real-Time Scheduling For Linear Tv Broadcasting: A Data-Driven Approach. International Neurourology Journal, 26(3), 20-25.
  • Souza, R. (2016). "Sentiment Classification Tools: A Comparative Analysis of SentiWordNet and SentiLex." Journal of Computational Linguistics, 23(1), 45-60.
  • Amo, S. (2003). "Text Mining and Pattern Discovery." Journal of Data Mining and Knowledge Discovery, 12(1), 91-105.
  • Bird, S., Loper, E., & Klein, E. (2022). Natural Language Processing with Python. O'Reilly Media.
  • Bhoir, H. and Jayamalini, K., 2021. Web Crawling on News Web Page using Different Frameworks.
  • Aljedaani, W., Rustam, F., Mkaouer, M.W., Ghallab, A., Rupapara, V., Washington, P.B., Lee, E. and Ashraf, I., 2022. Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry. Knowledge-Based Systems, 255, p.109780.
  • McKinney, W., 2022. Python for data analysis. " O'Reilly Media, Inc.".
    • " target="_blank"> BibTeX     |
  • Bollen, J., Mao, H. and Zeng, X., 2011. Twitter mood predicts the stock market. Journal of computational science, 2(1), pp.1-8.
  • Araújo, M., Gonçalves, P., & Benevenuto, F. (2013). "Comparing Sentiment Analysis Methods on Twitter." Proceedings of the International Conference on Web Intelligence, 157-164.
  • Ramos, P., & Benevenuto, F. (2016). "Sentiment Analysis on Twitter: A Comparison of Methods." Social Media and Society, 2(3), 78-90.
  • Araci, D., 2019. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. arXiv preprint arXiv:1908.10063.
  • Araci, D., 2019. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. arXiv preprint arXiv:1908.10063.
  • Ramagundam, S. (2022). Ai-Driven Real-Time Scheduling For Linear Tv Broadcasting: A Data-Driven Approach. International Neurourology Journal, 26(3), 20-25.
  • Souza, R. (2016). "Sentiment Classification Tools: A Comparative Analysis of SentiWordNet and SentiLex." Journal of Computational Linguistics, 23(1), 45-60.
  • Amo, S. (2003). "Text Mining and Pattern Discovery." Journal of Data Mining and Knowledge Discovery, 12(1), 91-105.
  • Bird, S., Loper, E., & Klein, E. (2022). Natural Language Processing with Python. O'Reilly Media.
  • Bhoir, H. and Jayamalini, K., 2021. Web Crawling on News Web Page using Different Frameworks.
  • Aljedaani, W., Rustam, F., Mkaouer, M.W., Ghallab, A., Rupapara, V., Washington, P.B., Lee, E. and Ashraf, I., 2022. Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry. Knowledge-Based Systems, 255, p.109780.
  • McKinney, W., 2022. Python for data analysis. " O'Reilly Media, Inc.".
    • " target="_blank">RIS     |
  • Bollen, J., Mao, H. and Zeng, X., 2011. Twitter mood predicts the stock market. Journal of computational science, 2(1), pp.1-8.
  • Araújo, M., Gonçalves, P., & Benevenuto, F. (2013). "Comparing Sentiment Analysis Methods on Twitter." Proceedings of the International Conference on Web Intelligence, 157-164.
  • Ramos, P., & Benevenuto, F. (2016). "Sentiment Analysis on Twitter: A Comparison of Methods." Social Media and Society, 2(3), 78-90.
  • Araci, D., 2019. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. arXiv preprint arXiv:1908.10063.
  • Araci, D., 2019. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. arXiv preprint arXiv:1908.10063.
  • Ramagundam, S. (2022). Ai-Driven Real-Time Scheduling For Linear Tv Broadcasting: A Data-Driven Approach. International Neurourology Journal, 26(3), 20-25.
  • Souza, R. (2016). "Sentiment Classification Tools: A Comparative Analysis of SentiWordNet and SentiLex." Journal of Computational Linguistics, 23(1), 45-60.
  • Amo, S. (2003). "Text Mining and Pattern Discovery." Journal of Data Mining and Knowledge Discovery, 12(1), 91-105.
  • Bird, S., Loper, E., & Klein, E. (2022). Natural Language Processing with Python. O'Reilly Media.
  • Bhoir, H. and Jayamalini, K., 2021. Web Crawling on News Web Page using Different Frameworks.
  • Aljedaani, W., Rustam, F., Mkaouer, M.W., Ghallab, A., Rupapara, V., Washington, P.B., Lee, E. and Ashraf, I., 2022. Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry. Knowledge-Based Systems, 255, p.109780.
  • McKinney, W., 2022. Python for data analysis. " O'Reilly Media, Inc.".
    • " target="_blank">CSV

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