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While artificial intelligence is successful in many applications that cover specific domains, for many commonsense problems there is still a large gap with human performance. Automated sentiment analysis is a typical example: while there are techniques tha ...
In this paper, we propose a classifier for predicting message-level sentiments of English micro-blog messages from Twitter. Our method builds upon the convolutional sentence embedding approach proposed by (Severyn and Moschitti, 2015a; Severyn and Moschitt ...
Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail. However, this improvement is relatively limited when compared to the gain observed in face embedding learning, which has ...
While social data is being widely used in various applications such as sentiment analysis and trend prediction, its sheer size also presents great challenges for storing, sharing and processing such data. These challenges can be addressed by data summariza ...
A popular application in Natural Language Processing (NLP) is the Sentiment Analysis (SA), i.e., the task of extracting contextual polarity from a given text. The social network Twitter provides an immense amount of text (called tweets) generated by users ...
What would someone from another culture think of this photograph I just took? Would they think my picture of this 'wilted flower' was also sentimentally positive or would they perceive it negatively instead? Or what if I wanted to find other photographs th ...
The technological environment that supports the learning process tends to be the main data source for Learning Analytics. However, this trend leaves out those parts of the learning process that are not computer-mediated. To overcome this problem, involving ...
This paper presents a novel application of sentiment analysis to recommender systems relying on explicit one-class user feedback (favorites or likes), namely joint models of unary feedback and sentiment of free-form user comments. This combination is achie ...
Emotion recognition in text has become an important research objective. It involves building classifiers capable of detecting human emotions for a specific application, for example, analyzing reactions to product launches, monitoring emotions at sports eve ...
In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often res ...
