A Hybrid Approach for Emotion Extraction
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 4
Abstract
With the growth of internet the amount of content that gets produced everyday has increased significantly. Many different types of textual/image documents are produced on varying types of platforms such as social media, news, blogs etc. This paper presents a hybrid approach for emotion extraction from the text documents. With such large amounts of textual content being produced every day, one cannot ignore the importance of mining and extracting opinions, sentiments and also emotions that are embedded in the text. The task of opinion mining, extraction and sentiment analysis has been extensively used in many fields. This is because emotions form an important part of any communication and opinion and have profound effect on the sentiments that the text represents. This makes emotion recognition or extraction from text as well as other media of communication such as speech images videos a crucial task. Emotion analysis is the task of making the computers makes sense of emotions. Emotion extraction is focused on delivery of the exact emotional state of a user at the time when the document was being prepared. In this paper, the main focus of the proposed work is to improve emotion extraction and the study of different approaches and methods that have already been developed and deployed for use. A number of approaches have been developed for emotion extraction and each of them has some strengths and weaknesses. Many machine learning techniques have been used such as Support Vector Machines, Naive Bayes classifier, Vector Space Model and others. Many others combine machine learning techniques with other rule-based techniques or keyword-based techniques. Our approach combines the SVM with k-NN algorithm while still employing the rule based technique to take the advantage of all while avoiding their weaknesses. Our algorithm will be implemented and evaluated using a standard benchmark GRIMM’s data set. The new approach will be evaluated on the basis of accuracy of the emotions that are assigned to the sentences in a document.
Authors and Affiliations
Mr. Ahefaz Pothiyawala, Mrs. Kavita R. Singh
Keywords
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- EP ID EP23751
- DOI http://doi.org/10.22214/ijraset.2017.4044
- Views 335
- Downloads 7
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