We are going through a phase of data evolution where a major portion of the data from our daily lives is now been stored on social media platforms. In recent years, social media has become ubiquitous and important for social networking and content sharing. Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. In the financial sector, sentiments are also of paramount importance, and this dissertation mainly focuses on the effect of sentiments from investors [3] on the behavior of stocks. The dissertation work leverages social data from Twitter and seeks the sentiment of certain investors. Once the sentiment of the tweets is calculated using an advanced sentiment analyzer, it is used as an additional attribute to the other fundamental properties of the stock. This dissertation demonstrates how incorporating the sentiments improves forecasting accuracy of predicting stock valuation. In addition, various experimental analysis on regression based statistical models are considered which show statistical measures to consider for effectively predicting the closing price of the stock. The Efficient Market Hypothesis (EMH) states that stock market prices are largely driven by additional information and follow a random walk pattern [7, 8, 37, 39, 41]. Though this hypothesis is widely accepted by the research community as a central paradigm governing the markets in general, several people have attempted to extract patterns in the way stock markets behave and respond to external stimuli. We test a hypothesis based on the premise of behavioral economics, that the emotions and moods of individuals basically the sentiments affect their decision-making process, thus, leading to a direct correlation between “public sentiment” and “market sentiment” [42, 43, 44, 45]. We first select key investors from Twitter [27, 28] whose sentiments matter and do sentiment analysis on the tweets pertaining to stock related information. Once we retrieve the sentiment for every stock, we combine this information with the other fundamental information about stocks and build different regression-based prediction models to predict their closing price.