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Sentiment Anaysis in Twitter

With the rapid growth of social networks and microblogging websites, communication between people from different cultural and psychological backgrounds became more direct, resulting in more and more “cyber” conflicts between these people. Consequently, hate speech is used more and more, to the point where it became a serious problem invading these open spaces. Hate speech refers to the use of positive or negative language, targeting a specific group of people sharing a common property, whether this property is their gender, their ethnic group or race or their believes and religion, etc. While most of the microblogging websites and online social networks forbid the use of hate speech, the size of these networks and websites makes it almost impossible to control all of their content. Therefore, it is necessary to detect such speech automatically and filter any content that presents hateful language. In this project we propose an approach to detect sentiment analysis in Twitter. Our approach is based on positive and negative words that are automatically collected from the training set. Our experiments show that our approach detecting whether a tweet is positive or negative (binary classification).