Mood Detection with Tweets
Emotional states of individuals, also known as moods, are central to the expression of thoughts, ideas, and opinions, and in turn, impact attitudes and behavior. Social media tools like Twitter is increasingly used by individuals to broadcast their day-to-day happenings or to report on an external event of interest, understanding the rich „landscape‟ of moods will help us better to interpret millions of individuals. This paper describes a Rule-Based approach, which detects the emotion or mood of the tweet and classifies the twitter message under the appropriate emotional category. The accuracy with the system is 85%. With the proposed system it is possible to understand the deeper levels of emotions i.e., finer grained instead of sentiment i.e., coarse-grained. The sentiment says whether the tweet is positive or negative but the proposed system gives the deeper information of tweet which has adverse uses in the field of Psychology, Intelligence Bureau, Social and Economic trends.
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