Financial market like the price of stock, share, gold, oil, mutual funds are affected by the news and posts on social media. In this work deep learning based models are proposed to predict the trend of financial market based on NLP analysis of the twitter handles of leaders of different fields. There are many models available to predict financial market based on only the historical data of the financial component but combining historical data with news and posts of the social media like Twitter is the main objective of the present work. Substantial improvement is shown in the result. The main features of the present work are- a) proposing completely generalized algorithm which is able to generate models for any twitter handle and any financial component, b) predicting the time window for a tweets effect on a stock price c) analyzing the effect of multiple twitter handles for predicting the trend. A detailed survey is done to find out the latest work in recent years in the similar field, find the research gap, and collect the required data for analysis and prediction. State-of-the-art algorithm is proposed and complete implementation with environment is given. An insightful trend of the result improvement considering the NLP analysis of twitter data on financial market components is shown. The Indian and USA financial markets are explored in the present work where as other markets can be taken in future. The socio-economic impact of the present work is discussed in conclusion.

翻译：金融市场（如股票、股份、黄金、石油、共同基金的价格）受社交媒体上的新闻和帖子影响。本研究提出基于深度学习的模型，通过分析不同领域领导者推特账号的自然语言处理（NLP）来预测金融市场趋势。现有许多模型仅基于金融成分的历史数据预测市场，但本研究的主要目标是将历史数据与社交媒体（如推特）的新闻和帖子相结合。结果显示显著改进。本研究的主要特点包括：a) 提出完全通用化的算法，能为任意推特账号和任意金融成分生成模型；b) 预测推文对股价影响的时间窗口；c) 分析多个推特账号对趋势预测的联合效应。本文进行了详细调研，以了解近年来相似领域的最新研究、发现研究差距，并收集所需数据用于分析和预测。提出了最先进的算法，并给出了完整的实现环境。通过展示推特数据NLP分析对金融市场成分的改进趋势，揭示了显著成果。本研究探索了印度和美国金融市场，未来可扩展至其他市场。结论部分讨论了本研究的社会经济影响。