Sentiment Analysis from Affective Multimodal Content

Abstract

Sentiment analysis is the computational study of opinions, appraisals, attitudes newlineand emotions towards the entities and their attributes. A basic task of sentiment newlineanalysis is to identify the sentiment polarity of the documents, sentences or aspects. newlineHuman affective states are considered to determine sentiment expressed. Generally, newlineusers express their opinions about the products or services in blog posts, shopping newlinesites or review sites. Such kind of opinion related contents are overwhelming and newlinegrowing exponentially which becomes a tedious work for the manufacturer to classify newlinethese contents manually. Moreover, people are expecting the opinion about the newlineentities in aspects level. Hence, it is necessary to construct an automatic sentiment newlineanalyzer which automatically identifies the sentiment polarity of the newlinedocuments/aspects in bipolarity level and multi polarity level. With the development newlineof the social networking sites, people are able to publicly express their opinions newlinethrough social media. This provided a rich source of feedback and analysis of newlineemotions and stimulated the development of automatic sentimental analysis. newlineTherefore, the supervised classification algorithm has been proved promising and newlinehence widely used in multi sentiment analysis tasks. In this research work, four types newlineof extensive methodologies have been used for providing an efficient multimodal newlinesentiment analysis (MSA). In a text sentiment analysis, the important procedure is the newlineprocess of finding the polarities of a specified text as either positive or negative. In newlinethis study, twitter comments are used as an input. SentiWordNet technique is used to newlineextract the features from the text, where the classification and identification of newlinepolarity for those extracted features are processed by distance based classifier. newlineHowever, the emotion of the end-user is not analyzed in this sentiment analysis, newlinewhich motivates to identify the sentiments using speech signals. newlineSpeech Emotion Recognition (SER) is a major research area to identify the newlineemotion of human