Bio-Inspired Computing Techniques for Social Stock Recommendations
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The tremendous growth of the social networks has paved way for social interactions of investing communities about a company's stock performance. Web 2.0 platforms give people more power over the way they share information and exchange stock opinions. Investors are able to share their comments on stocks using the social media platforms. These interactions are captured and mined to produce advice on investing which helps retail investors to do prospective investments to increase profits. This book proposes a novel stock recommendation methodology using Bio-inspired Computing Techniques. This met...
The tremendous growth of the social networks has paved way for social interactions of investing communities about a company's stock performance. Web 2.0 platforms give people more power over the way they share information and exchange stock opinions. Investors are able to share their comments on stocks using the social media platforms. These interactions are captured and mined to produce advice on investing which helps retail investors to do prospective investments to increase profits. This book proposes a novel stock recommendation methodology using Bio-inspired Computing Techniques. This method extracts sentiments from the investor's stock reviews crawled from Twitter and performs the sentiment analysis, which is optimized by the Bio-Inspired Computing Techniques.
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