Complementary Aspect-Based Opinion Mining across Asymmetric Collections Using CAMEL

We propose CAMEL, a novel theme model for complementary aspect-based opinion mining across asymmetric collections CAMEL picks up data complementarity by demonstrating both normal and explicit aspectes crosswise over assortments, while keeping all the comparing suppositions for contrastive investigation. An auto-labeling scheme called AME is likewise proposed to help separate among viewpoint and opinion words without elaborative human marking, which are additionally upgraded by including word implanting based comparability as another element. In addition, CAMEL-DP, a nonparametric option in contrast to CAMEL is likewise proposed dependent on coupled Dirichlet Processes. Broad examinations on genuine world multi-collection audits information exhibit the prevalence of our strategies over aggressive baselines. This is especially obvious when the data shared by various assortments turns out to be genuinely divided

A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews

Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents. We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.Comment: Second Grand Challenge and Workshop on Multimodal Language ACL 202

Cross-Domain Labeled LDA for Cross-Domain Text Classification

Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the feature distribution differences of the two domains. Most existing studies explicitly minimize such differences by an exact alignment mechanism (aligning features by one-to-one feature alignment, projection matrix etc.). Such exact alignment, however, will restrict models' learning ability and will further impair models' performance on classification tasks when the semantic distributions of different domains are very different. To address this problem, we propose a novel group alignment which aligns the semantics at group level. In addition, to help the model learn better semantic groups and semantics within these groups, we also propose a partial supervision for model's learning in source domain. To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and Reuters dataset, extensive quantitative (classification, perplexity etc.) and qualitative (topic detection) experiments are conducted to show the effectiveness of the proposed group alignment and partial supervision.Comment: ICDM 201

Framework for opinion as a service on review data of customer using semantics based analytics

At Opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as Opinion-as-a-Service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance

Unionization method for changing opinion in sentiment classification using machine learning

Sentiment classification aims to determine whether an opinionated text expresses a positive, negative or neutral opinion. Most existing sentiment classification approaches have focused on supervised text classification techniques. One critical problem of sentiment classification is that a text collection may contain tens or hundreds of thousands of features, i.e. high dimensionality, which can be solved by dimension reduction approach. Nonetheless, although feature selection as a dimension reduction method can reduce feature space to provide a reduced feature subset, the size of the subset commonly requires further reduction. In this research, a novel dimension reduction approach called feature unionization is proposed to construct a more reduced feature subset. This approach works based on the combination of several features to create a more informative single feature. Another challenge of sentiment classification is the handling of concept drift problem in the learning step. Users’ opinions are changed due to evolution of target entities over time. However, the existing sentiment classification approaches do not consider the evolution of users’ opinions. They assume that instances are independent, identically distributed and generated from a stationary distribution, even though they are generated from a stream distribution. In this study, a stream sentiment classification method is proposed to deal with changing opinion and imbalanced data distribution using ensemble learning and instance selection methods. In relation to the concept drift problem, another important issue is the handling of feature drift in the sentiment classification. To handle feature drift, relevant features need to be detected to update classifiers. Since proposed feature unionization method is very effective to construct more relevant features, it is further used to handle feature drift. Thus, a method to deal with concept and feature drifts for stream sentiment classification was proposed. The effectiveness of the feature unionization method was compared with the feature selection method over fourteen publicly available datasets in sentiment classification domain using three typical classifiers. The experimental results showed the proposed approach is more effective than current feature selection approaches. In addition, the experimental results showed the effectiveness of the proposed stream sentiment classification method in comparison to static sentiment classification. The experiments conducted on four datasets, have successfully shown that the proposed algorithm achieved better results and proving the effectiveness of the proposed method

Video Analysis for Understanding Human Actions and Interactions

Each time that we act, our actions are not just conditioned by the spatial information, e.g., objects, people, and the scene where we are involved. These actions are also conditioned temporally with the previous actions that we have done. Indeed, we live in an evolving and dynamic world. To understand what a person is doing, we reason jointly over spatial and temporal information. Intelligent systems that interact with people and perform useful tasks will also require this ability. In light of this need, video analysis has become, in recent years, an essential field in computer vision, providing to the community a wide range of tasks to solve. In this thesis, we make several contributions to the literature of video analysis, exploring different tasks that aim to understand human actions and interactions. We begin by considering the challenging problem of human action anticipation. In this task, we seek to predict a person's action as early as possible before it is completed. This task is critical for applications where machines have to react to human actions. We introduce a novel approach that forecasts the most plausible future human motion by hallucinating motion representations. Then, we address the challenging problem of temporal moment localization. It consists of finding the temporal localization of a natural-language query in a long untrimmed video. Although the queries could be anything that is happening within the video, the vast majority of them describe human actions. In contrast with the propose and rank approaches, where methods create or use predefined clips as candidates, we introduce a proposal-free approach that localizes the query by looking at the whole video at once. We also consider the temporal annotations' subjectivity and propose a soft-labelling using a categorical distribution centred on the annotated start and end. Equipped with a proposal-free architecture, we tackle the temporal moment localization introducing a spatial-temporal graph. We found that one of the limitations of the existing methods is the lack of spatial cues involved in the video and the query, i.e., objects and people. We create six semantically meaningful nodes. Three that are feed with visual features of people, objects, and activities, and the other three that capture the relationship at the language level of the "subject-object,'' "subject-verb," and "verb-object." We use a language-conditional message-passing algorithm to capture the relationship between nodes and create an improved representation of the activity. A temporal graph uses this new representation to determine the start and end of the query. Last, we study the problem of fine-grained opinion mining in video review using a multi-modal setting. There is increasing use of video as a source of information for guidance in the shopping process. People use video reviews as a guide to answering what, why, and where to buy something. We tackle this problem using the three different modalities inherently present in a video ---audio, frames, and transcripts--- to determine the most relevant aspect of the product under review and the sentiment polarity of the reviewer upon that aspect. We propose an early fusion mechanism of the three modalities. In this approach, we fuse the three different modalities at the sentence level. It is a general framework that does not lay in any strict constraints on the individual encodings of the audio, video frames and transcripts