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STOCKSENTIX: A MACHINE LEARNING APPROACH TO STOCKMARKET
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 11 | Nov 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 361 STOCKSENTIX: A MACHINE LEARNING APPROACH TO STOCKMARKET Sahil Chaudhari1, Mayuresh Dhanawade2, Anay Aher3 , Rakesh Singh4 , Prof. Anjalidevi Patil5 Department of Computer Science & Engineering AI & IOT, SMT Indira Gandhi College of Engineering --------------------------------------------------------------------------***------------------------------------------------------------------------ Abstract: In today's information-driven financial world, timely access to and analysis of stock market news is critical for informed decision-making. This research paper presents a comprehensive approach to collecting and analyzing stock market news articles through web scraping with Beautiful Soup. We then employed Pandas to preprocess and structure the data for sentiment analysis, and Matplotlib was utilized to create visual representations of sentiment trends. The project aims to provide investors and traders with valuable insights into market sentiment for better decision-making. 1. Introduction: The financial markets operate in a dynamic and rapidly changing environment where real- time access to information can make or break an investment. Sentiment analysis of stock market news has emerged as a valuable tool for understanding market sentiment and predicting market trends. This research project outlines a novel approach to gathering, processing, and analyzing stock market news articles. It utilizes web scraping techniques, data preprocessing with Pandas, sentiment analysis, and data visualization with Matplotlib. 2. Web Scraping Using BeautifulSoup: Web scraping is the process of extracting data from websites. We employed BeautifulSoup, a Python library, to scrape stock market news articles from various online sources. By sending HTTP requests and parsing HTML content, we collected textual data for analysis. We have also applied advanced scraping techniques such as handling AJAX requests and implementing anti-bot measures for robust data retrieval. 3. Data Preprocessing Using Pandas: - Data preprocessing is a crucial step to ensure the quality and consistency of data. We used Pandas, a powerful data manipulation library in Python, to clean, structure, and format the collected data. Data preprocessing tasks included handling missing values, encoding text data, and ensuring data integrity. Moreover, we employed data imputation methods and conducted exploratory data analysis (EDA) to gain deeper insights into the dataset. 4. Data Visualization Using Matplotlib: - Sentiment analysis is most effective when it is presented in an easily interpretable format. Matplotlib, a widely used data visualization library, was employed to create visual representations of sentiment trends. These visualizations provide a clear and concise way to identify sentiment patterns and trends within the stock market news. we used interactive visualization tools like Plotly for more engaging and interactive sentiment trend representations. 5. Sentiment Analysis: - Sentiment analysis was performed to assess the emotional tone and polarity of each news article. Sentiment was classified into three categories: positive, negative, and neutral. The sentiment scoring was accomplished using a predefined lexicon, machine learning models, or hybrid approaches depending on the specific use case. Sentiment analysis is a sophisticated natural language processing (NLP) technique that entails the use of computational algorithms and machine learning models to discern, classify, and quantify the subjective sentiments expressed within textual data. We have incorporated deep learning techniques, such as LSTM networks, for improved sentiment analysis performance. Text Representation: Raw text data is transformed into a format suitable for analysis. This often involves techniques like tokenization (breaking text into individual words or tokens), stemming (reducing words to their base or root form), and vectorization (converting text into numerical vectors). We also explored word embedding techniques like Word2Vec to capture semantic relationships in the text. Feature Extraction: Relevant features are extracted from the text. This step identifies key elements, such as words or phrases, that contribute to the sentiment expressed. Feature extraction is crucial for training machine learning models. experimented with feature selection techniques to optimize model performance and reduce dimensionality.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 11 | Nov 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 362 Model Selection: Various machine learning models can be used for sentiment analysis, including traditional classifiers like Support Vector Machines (SVM) or more advanced techniques like recurrent neural networks (RNNs) and transformers. The choice depends on the complexity of the data and the desired performance. We conducted model hyperparameter tuning to enhance the performance of our sentiment analysis models. 6. System Architecture: 7. Result: Sentiment Analysis 8. Conclusion: The project illustrates the potential of web scraping, data preprocessing, sentiment analysis, and data visualization techniques in the context of stock market news analysis. By tracking sentiment trends, market participants can make more informed decisions and potentially gain a competitive edge in the financial markets. This research highlights the practical applications of data- driven insights and underscores their value in making financial decisions in a rapidly changing and highly competitive market. Future work can explore advanced sentiment analysis techniques, including the integration of natural language processing and machine learning models. The integration of sentiment trendanalysis withstockpricedatacanprovide a more comprehensive view of market sentiment's impact on stock price movements. In a world where information drives financial markets, this project contributes to the growing field of sentiment analysis. By providing a practical and accessible solution for collecting, analyzing, and visualizing sentiment in stock market news, the project equips market participants with a powerful tool for informed decision-making and risk management.
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 11 | Nov 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 363 REFERENCES [1] A.Pak and P. Paroubek. „Twitter as a Corpus for Sentiment Analysis and Opinion Mining". In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 2010, pp.1320-1326 [2] R. Parikh and M. Movassate, “Sentiment Analysis of User- Generated Twitter Updates using Various Classification Techniques",CS224N Final Report, 2009 sentiment analysis methods, applications, and challenges. Artif Intell Rev 55, 5731 5780(2022). https://doi.org/10.1007/s10462-022-10144 [5] Bifet and E. Frank, "Sentiment Knowledge Discovery in Twitter Streaming Data", In Proceedings of the 13th International Conference on Discovery Science, Berlin, Germany: Springer,2010, pp. 1-15. [6] Agarwal, B. Xie, I. Vovsha, O. Rambow, R. Passonneau, “Sentiment Analysis of Twitter Data", In Proceedings of the ACL 2011 Workshop on Languages in Social Media,2011 , pp. 30-38 [7] Po-Wei Liang, Bi-Ru Dai, “Opinion Mining on Social MediaData", IEEE 14th International Conference on Mobile Data Management,Milan, Italy, June 3 - 6, 2013, pp 91-96, ISBN: 978-1-494673-6068-5, http://doi.ieeecomputersociety.org/10.110 9/MDM. [8] Oscar Araque (Polytechnic University of Madrid) , Lorenzo Gatti (University of Twente) , Marco Guerini (AdeptMind Scholar, Bruno Kessler Institute), Jacopo Staiano (Recital AI) [9] MELD: A Multimodal Multi-Party Dataset for Emotion Recognition Soujanya Poria (SUTD) , Devamanyu Hazarika (National University of Singapore), Navonil Majumder (National Polytechnic Institute of Mexico) , Gautam Naik (Nanyang Technological University) , Erik Cambria (Nanyang Technological University) , Rada Mihalcea (University of Michigan) [10] Hassan Raza, M. Faizan, Ahsan Hamza, Ahmed Mushtaq and Naeem Akhtar, “Scientific Text Sentiment Analysis using Machine Learning Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.01 01222 [11] Baccianella, Stefano & Esuli, Andrea & Sebastiani, Fabrizio. (2010). SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining.. Proceedings of LREC. 10 [3] Go, R. Bhayani, L.Huang. “Twitter Sentiment Classification Using Distant Supervision". Stanford University, Technical Paper,2009 [4] Wankhade, M., Rao, A.C.S. & Kulkarni, C. A survey on
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