دانلود مقاله ISI انگلیسی شماره 124714
ترجمه فارسی عنوان مقاله

استخراج احساسات از متن چند زبانه با استفاده از پردازش متن هوشمند و زبان شناسی محاسباتی

عنوان انگلیسی
Extraction of emotions from multilingual text using intelligent text processing and computational linguistics
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
124714 2017 11 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Computational Science, Volume 21, July 2017, Pages 316-326

ترجمه کلمات کلیدی
استخراج احساسی، فراگیری ماشین، استخراج متن، توییتر، طبقه بندی، پردازش زبان طبیعی،
کلمات کلیدی انگلیسی
Emotion extraction; Machine learning; Text mining; Twitter; Classification; Natural language processing;
پیش نمایش مقاله
پیش نمایش مقاله  استخراج احساسات از متن چند زبانه با استفاده از پردازش متن هوشمند و زبان شناسی محاسباتی

چکیده انگلیسی

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.