Amir Hossein Razavi

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Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. In the same time, flames (such as rants, taunts, and squalid phrases) are offensive/abusive phrases which might attack or offend the users for a variety of reasons. An automatic discriminative software with a sensitivity parameter for(More)
The purpose of this work is to reduce the workload of human experts in building systematic reviews from published articles, used in evidence-based medicine. We propose to use a committee of classifiers to rank biomedical abstracts based on the predicted relevance to the topic under review. In our approach, we identify two subsets of abstracts: one that(More)
In this article, we present a novel statistical representation method for knowledge extraction from a corpus containing short texts. Then we introduce the contrast parameter which could be adjusted for targeting different conceptual levels in text mining and knowledge extraction. The method is based on second order co-occurrence vectors whose efficiency for(More)
We describe a project undertaken by an interdisciplinary team combining researchers in sleep psychology and in Natural Language Processing/Machine Learning. The goal is sentiment analysis on a corpus containing short textual descriptions of dreams. Dreams are categorized in a four-level scale of positive and negative sentiments. We chose a four scale(More)
Innovations, opinions, ideas, recommendations or tendencies emerge in a variety of social networks. They can either disappear quickly or propagate and create considerable impact on the network. Their disappearance may also spread from one node to another across the network creating cascading behavior. Cascading phenomenon is mainly analyzed either by(More)
In this article, we present a novel document annotation method that can be applied on corpora containing short documents such as social media texts. The method applies Latent Dirichlet Allocation (LDA) on a corpus to initially infer some topical word clusters. Each document is assigned one or more topic clusters automatically. Further document annotation is(More)
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