A Combined Approach for Extracting Financial Instrument-Specific Investor Sentiment from Weblogs

Investor sentiment about future returns of financial instruments is a highly relevant information source for investment managers and other stakeholders in the financial industry. Investor sentiments are abundant in financial blog texts. Making use of these sentiments constitutes a massive information management challenge when considering the millions of blog articles with everchanging and growing amounts of information that need to be acquired and interpreted. We propose a novel approach for investor sentiment extraction from blogs by combining machine-learning on the document-level and knowledgebased information extraction on the sentence-level. The proposed artifact is a financial instrument-specific investor sentiment extraction method, which we apply to a set of blog articles. The evaluation suggests that the combined approach achieves a higher precision compared to a standalone knowledge-based approach

Modeling movements in oil, gold, forex and market indices using search volume index and Twitter sentiments

Study of the forecasting models using large scale microblog discussions and the search behavior data can provide a good insight for better understanding the market movements. In this work we collected a dataset of 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011. We model a set of comprehensive causative relationships over this dataset for various market securities like equity (Dow Jones Industrial Average-DJIA and NASDAQ-100), commodity markets (oil and gold) and Euro Forex rates. We also investigate the lagged and statistically causative relations of Twitter sentiments developed during active trading days and market inactive days in combination with the search behavior of public before any change in the prices/ indices. Our results show extent of lagged significance with high correlation value upto 0.82 between search volumes and gold price in USD. We find weekly accuracy in direction (up and down prediction) uptil 94.3% for DJIA and 90% for NASDAQ-100 with significant reduction in mean average percentage error for all the forecasting models

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Sentiment analysis: the case of twitch chat - Mining user feedback from livestream chats

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementIn a world where users often share their thoughts and opinions through online communication channels, applications that can tap into these channels as to extract consumer feedback have become increasingly valuable. Traditional marketing research techniques such as interviews or surveys offer results that pale in comparison to sentiment analysis applications that can extract organic feedback from an extremely large selection, with very little resources and in real-time. This thesis focuses on proposing and developing one of these tools that targets livestreams, which have, over the years, seen a massive increase in popularity from both a user-base standpoint as well as brand involvement. We chose the livestreaming platform “Twitch” as the target of research and developed a sentiment analysis model, using rule-based approaches, capable of interpreting user chat messages and identifying whether those messages are negative, positive or neutral. Additionally, an application was developed to better view and analyze the results of the model. By segmenting our results by product reveal, we also exhibit how the application allows for the extraction of various insights about the public’s opinion of that product