Most sentiment analysis approaches rely on machine-learning techniques, using a bag-of-words (BoW) document representation as their basis. In this paper, we examine whether a more fine-grained representation of documents as sequences of emotionally-annotated sentences can increase document classification accuracy. Experiments conducted on a sentence and document level annotated corpus show that...

We present the system we built for participating in the PAN-2016 Author Profiling Task [9]. The task asked to predict the gender and the age group of a person given several samples of his/her writing, and it was offered for three different languages: English, Spanish, and Dutch. We participated in both subtasks, for all three languages. Our approach focused on extracting genre-agnostic features...

In this paper we propose a novel character representation for scene text recognition. In order to recognize each individual character, we first adopt a bag-of-words approach, in which the rotation-invariant circular Fourier-HOG features are densely extracted from an individual character and compressed into middle level features. Then we train a set of two-class linear Support Vector Machines in...

This paper presents a novel and efficient framework for group activity analysis. People in a scene can be intuitively represented by an undirected graph where vertices are people and the edges between two people are weighted by how much they are interacting. Social signaling cues are used to describe the degree of interaction between people. We propose a graph-based clustering algorithm to disc...

In this work, we propose a novel approach to extract sentiment-bearing expression features derived from dependency structures. Rather than directly use dependency relations generated by a parser, we propose a set of heuristic rules to detect both explicit and implicit negations in the text. Then, three patterns are defined to support generalized sentiment-bearing expressions. By altering existi...

Document classification is a machine learning application that has been as impactful as it has been successful in a myriad of domains and applications. However, when the documents being classified are large and highly-complex, and when the set of potential classes is large as well, these models could be improved by incorporating more information about the documents’ overall structure. Most appr...

This paper presents a novel way to perform probabilistic modeling of occupancy patterns from a sensor network. The approach is based on the Latent Dirichlet Allocation (LDA) model. The application of the LDA model is shown using a real dataset of occupancy logs from the sensor network of a modern office building. LDA is a generative and unsupervised probabilistic model for collections of discre...

This paper presents a new framework for visual place recognition that incrementally learns models of each place and offers adaptability to dynamic elements in a scene. Traditional bag-of-features image-retrieval approaches to place recognition treat images in a holistic manner and are typically not capable of dealing with sub-scene dynamics, such as structural changes to a building facade or th...

In this paper, we proposed a classification method of spectators’ state in video sequences by voting of facial expressions and face directions. The task of this paper is to classify the state of the spectators in a given video sequence into “Positive Scene” or “Negative Scene”, and “Watching Seriously” or “Not Watching Seriously”. The proposed classifier is designed by a “bag-of-visual-words” a...

The method based on Bag-of-visual-Words (BoW) deriving from local keypoints has recently appeared promising for video annotation. Spatial partition scheme has critical impact to the performance of BoW method. In this paper, we propose a new adaptive annular spatial partition scheme. The proposed scheme firstly determines the centroid of partition according to the distribution of keypoints. And ...