Extraction of emotions from multilingual text using intelligent text processing and computational linguistics

Research paper by Vinay Kumar Jain, Shishir Kumar, Steven Lawrence Fernandes

Indexed on: 20 Mar '17Published on: 01 Feb '17Published in: Journal of Computational Science


Extraction of Emotions from Multilingual Text posted on social media by different categories of users is one of the crucial tasks in the field of opining mining and sentiment analysis. Every major event in the world has an online presence and social media. Users use social media platforms to express their sentiments and opinions towards it. In this paper, an advanced framework for detection of emotions of users in Multilanguage text data using emotion theories has been presented, which deals with linguistics and psychology. The emotion extraction system is developed based on multiple features groups for the better understanding of emotion lexicons. Empirical studies of three real-time events in domains like a Political election, healthcare, and sports are performed using proposed framework. The technique used for dynamic keywords collection is based on RSS (Rich Site Summary) feeds of headlines of news articles and trending hashtags from Twitter. An intelligent data collection model has been developed using dynamic keywords. Every word of emotion contained in a tweet is important in decision making and hence to retain the importance of multilingual emotional words, effective pre-processing technique has been used. Naive Bayes algorithm and Support Vector Machine (SVM) are used for fine-grained emotions classification of tweets. Experiments conducted on collected data sets, show that the proposed method performs better in comparison to corpus-driven approach which assign affective orientation or scores to words. The proposed emotion extraction framework performs better on the collected dataset by combining feature sets consisting of words from publicly available lexical resources. Furthermore, the presented work for extraction of emotion from tweets performs better in comparisons of other popular sentiment analysis techniques which are dependent of specific existing affect lexicons.

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