EMOTION MINING IN ENGLISH TEXT USING BAYES & SVM APPROACH

Emotions have captivated researchers for years, as is obvious in the huge body of research work related to emotion in area of mental characteristics, language, socialism, and interaction. Human emotion manifests itself in the form of facial expressions, speech utterances, writings, and in gestures and actions. As a result, technical research in emotion has been pursued along several proportions and has drawn upon research from various areas. This paper results in the chore of emotion gratitude by attempting to robotically learn emotions from text. In this paper, a new technique to mine the emotions from an English text has been introduced. We have used 10 categories from which we can extract the motions. Proposed system use bays and SVM approach to perform the given task. The accuracy of the proposed system is better as compared to the existing system.

Emotion Mining: from Unimodal to Multimodal Approaches

In the last decade, Sentiment Analysis and Affective Computing have found applications in different domains. In particular, the interest of extracting emotions in healthcare is demonstrated by the various applications which encompass patient monitoring and adverse events prediction. Thanks to the availability of large datasets, most of which are extracted from social media platforms, several techniques for extracting emotion and opinion from different modalities have been proposed, using both unimodal and multimodal approaches. After introducing the basic concepts related to emotion theories, mainly borrowed from social sciences, the present work reviews three basic modalities used in emotion recognition, i.e. textual, audio and video, presenting for each of these i) some basic methodologies, ii) some among the widely used datasets for the training of supervised algorithms and iii) briefly discussing some deep Learning architectures. Furthermore, the paper outlines the challenges and existing resources to perform a multimodal emotion recognition which may improve performances by combining at least two unimodal approaches. architecture to perform multimodal emotion recognition.