Improving Sentiment Analysis in Arabic Using Word Representation
نویسندگان
چکیده
The complexities of Arabic language in morphology, orthography and dialects makes sentiment analysis for Arabic more challenging. Also, text feature extraction from short messages like tweets, in order to gauge the sentiment, makes this task even more difficult. In recent years, deep neural networks were often employed and showed very good results in sentiment classification and natural language processing applications. Word embedding, or word distributing approach, is a current and powerful tool to capture together the closest words from a
منابع مشابه
Using Machine Learning Algorithms for Automatic Cyber Bullying Detection in Arabic Social Media
Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine l...
متن کاملImproving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings
Most of existing work learn sentiment-specific word representation for improving Twitter sentiment classification, which encoded both n-gram and distant supervised tweet sentiment information in learning process. They assume all words within a tweet have the same sentiment polarity as the whole tweet, which ignores the word its own sentiment polarity. To address this problem, we propose to lear...
متن کاملSentiment Lexicons for Arabic Social Media
Existing Arabic sentiment lexicons have low coverage—only a few thousand entries. In this paper, we present several large sentiment lexicons that were automatically generated using two different methods: (1) by using distant supervision techniques on Arabic tweets, and (2) by translating English sentiment lexicons into Arabic using a freely available statistical machine translation system. We c...
متن کاملImproving Twitter Sentiment Classification Using Topic-Enriched Multi-Prototype Word Embeddings
It has been shown that learning distributed word representations is highly useful for Twitter sentiment classification. Most existing models rely on a single distributed representation for each word. This is problematic for sentiment classification because words are often polysemous and each word can contain different sentiment polarities under different topics. We address this issue by learnin...
متن کاملA Supervised Method for Constructing Sentiment Lexicon in Persian Language
Due to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. This problem deals with information extraction and knowledge discovery from textual data using natural language processing has attracted the attention of many researchers. Construction of sentiment lexicon as a valuable language resource is a one of the imp...
متن کامل