Analyzing Emotional States Induced by News Articles with Latent Semantic Analysis
Emotions are reflected both in verbal and written communication. If in the first case they can be easier to trace due to some specific features (body language, voice tone or inflections), in the second it can be quite tricky to grasp the underlying emotions carried by a written text. Therefore we propose a novel automatic method for analyzing emotions induced by texts, more specifically a reader’s most likely emotional state after reading a news article. In other words, our goal is to determine how reading a piece of news affects a person’s emotional state and to adjust these values based on his/her current state. From a more technical perspective, our system (Emo2 – Emotions Monitor) combines a context independent approach (actual evaluation of the news employing specific natural language processing techniques and Latent Semantic Analysis) with the influences of user’s present emotional state estimated through his/her specific feedback for building a more accurate image of a person’s emotional state.
Keywordsemotional state Latent Semantic Analysis automatic evaluation of news articles
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