ﻻ يوجد ملخص باللغة العربية
With the proliferation of social media over the last decade, determining peoples attitude with respect to a specific topic, document, interaction or events has fueled research interest in natural language processing and introduced a new channel called sentiment and emotion analysis. For instance, businesses routinely look to develop systems to automatically understand their customer conversations by identifying the relevant content to enhance marketing their products and managing their reputations. Previous efforts to assess peoples sentiment on Twitter have suggested that Twitter may be a valuable resource for studying political sentiment and that it reflects the offline political landscape. According to a Pew Research Center report, in January 2016 44 percent of US adults stated having learned about the presidential election through social media. Furthermore, 24 percent reported use of social media posts of the two candidates as a source of news and information, which is more than the 15 percent who have used both candidates websites or emails combined. The first presidential debate between Trump and Hillary was the most tweeted debate ever with 17.1 million tweets.
It is very current in today life to seek for tracking the people opinion from their interaction with occurring events. A very common way to do that is comments in articles published in newspapers web sites dealing with contemporary events. Sentiment
Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge, such as sentiment words and aspect-sentiment pairs, is ignored in the process of pre-training, despite the fact that they
Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent neural networks (RNNs) or convolutional networks (CNNs). This is a probl
With the development of the E-commerce and reviews website, the comment information is influencing peoples life. More and more users share their consumption experience and evaluate the quality of commodity by comment. When people make a decision, the
Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment analysis.