| dc.contributor.author | Edirisooriya, Akhila | |
| dc.date.accessioned | 2026-03-23T05:37:52Z | |
| dc.date.available | 2026-03-23T05:37:52Z | |
| dc.date.issued | 2025 | |
| dc.identifier.citation | Edirisooriya, Akhila (2025) PredectivePluse: Sentiment Analysis for Market Prediction. BSc. Dissertation, Informatics Institute of Technology | en_US |
| dc.identifier.issn | 2019037 | |
| dc.identifier.uri | http://dlib.iit.ac.lk/xmlui/handle/123456789/3019 | |
| dc.description.abstract | This paper tackles the challenge of predicting stock market trends by integrating sentiment analysis with Long Short-Term Memory (LSTM) networks. Traditional methods, relying on historical price data, often overlook the immediate impact of market sentiment on stock movements. To address this, the author incorporate real-time sentiment data from sources such as social media and financial news, using advanced natural language processing techniques to extract actionable sentiment indicators. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Stock Market Prediction | en_US |
| dc.subject | Sentiment Analysis | en_US |
| dc.subject | LSTM Networks | en_US |
| dc.title | PredectivePluse: Sentiment Analysis for Market Prediction | en_US |
| dc.type | Thesis | en_US |