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Register a new userMultitask Learning for Fine-Grained Twitter Sentiment Analysis
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Added by
Georgios Balikas
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately. We argue that such classification tasks are correlated and we propose a multitask approach based on a recurrent neural network that benefits by jointly learning them. Our study demonstrates the potential of multitask models on this type of problems and improves the state-of-the-art results in the fine-grained sentiment classification problem.
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Cross-media retrieval is to return the results of various media types corresponding to the query of any media type. Existing researches generally focus on coarse-grained cross-media retrieval. When users submit an image of Slaty-backed Gull as a query, coarse-grained cross-media retrieval treats it as Bird, so that users can only get the results of Bird, which may include other bird species with similar appearance (image and video), descriptions (text) or sounds (audio), such as Herring Gull. Such coarse-grained cross-media retrieval is not consistent with human lifestyle, where we generally have the fine-grained requirement of returning the exactly relevant results of Slaty-backed Gull instead of Herring Gull. However, few researches focus on fine-grained cross-media retrieval, which is a highly challenging and practical task. Therefore, in this paper, we first construct a new benchmark for fine-grained cross-media retrieval, which consists of 200 fine-grained subcategories of the Bird, and contains 4 media types, including image, text, video and audio. To the best of our knowledge, it is the first benchmark with 4 media types for fine-grained cross-media retrieval. Then, we propose a uniform deep model, namely FGCrossNet, which simultaneously learns 4 types of media without discriminative treatments. We jointly consider three constraints for better common representation learning: classification constraint ensures the learning of discriminative features, center constraint ensures the compactness characteristic of the features of the same subcategory, and ranking constraint ensures the sparsity characteristic of the features of different subcategories. Extensive experiments verify the usefulness of the new benchmark and the effectiveness of our FGCrossNet. They will be made available at https://github.com/PKU-ICST-MIPL/FGCrossNet_ACMMM2019.
Product search has been a crucial entry point to serve people shopping online. Most existing personalized product models follow the paradigm of representing and matching user intents and items in the semantic space, where finer-grained matching is totally discarded and the ranking of an item cannot be explained further than just user/item level similarity. In addition, while some models in existing studies have created dynamic user representations based on search context, their representations for items are static across all search sessions. This makes every piece of information about the item always equally important in representing the item during matching with various user intents. Aware of the above limitations, we propose a review-based transformer model (RTM) for personalized product search, which encodes the sequence of query, user reviews, and item reviews with a transformer architecture. RTM conducts review-level matching between the user and item, where each review has a dynamic effect according to the context in the sequence. This makes it possible to identify useful reviews to explain the scoring. Experimental results show that RTM significantly outperforms state-of-the-art personalized product search baselines.
In the Click-Through Rate (CTR) prediction scenario, users sequential behaviors are well utilized to capture the user interest in the recent literature. However, despite being extensively studied, these sequential methods still suffer from three limitations. First, existing methods mostly utilize attention on the behavior of users, which is not always suitable for CTR prediction, because users often click on new products that are irrelevant to any historical behaviors. Second, in the real scenario, there exist numerous users that have operations a long time ago, but turn relatively inactive in recent times. Thus, it is hard to precisely capture users current preferences through early behaviors. Third, multiple representations of users historical behaviors in different feature subspaces are largely ignored. To remedy these issues, we propose a Multi-Interactive Attention Network (MIAN) to comprehensively extract the latent relationship among all kinds of fine-grained features (e.g., gender, age and occupation in user-profile). Specifically, MIAN contains a Multi-Interactive Layer (MIL) that integrates three local interaction modules to capture multiple representations of user preference through sequential behaviors and simultaneously utilize the fine-grained user-specific as well as context information. In addition, we design a Global Interaction Module (GIM) to learn the high-order interactions and balance the different impacts of multiple features. Finally, Offline experiment results from three datasets, together with an Online A/B test in a large-scale recommendation system, demonstrate the effectiveness of our proposed approach.
As microblogging services like Twitter are becoming more and more influential in todays globalised world, its facets like sentiment analysis are being extensively studied. We are no longer constrained by our own opinion. Others opinions and sentiments play a huge role in shaping our perspective. In this paper, we build on previous works on Twitter sentiment analysis using Distant Supervision. The existing approach requires huge computation resource for analysing large number of tweets. In this paper, we propose techniques to speed up the computation process for sentiment analysis. We use tweet subjectivity to select the right training samples. We also introduce the concept of EFWS (Effective Word Score) of a tweet that is derived from polarity scores of frequently used words, which is an additional heuristic that can be used to speed up the sentiment classification with standard machine learning algorithms. We performed our experiments using 1.6 million tweets. Experimental evaluations show that our proposed technique is more efficient and has higher accuracy compared to previously proposed methods. We achieve overall accuracies of around 80% (EFWS heuristic gives an accuracy around 85%) on a training dataset of 100K tweets, which is half the size of the dataset used for the baseline model. The accuracy of our proposed model is 2-3% higher than the baseline model, and the model effectively trains at twice the speed of the baseline model.
Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although there is some generalization analysis on different specific algorithms under the empirical risk minimization principle, a unifying analysis of vector-valued learning under a regularization framework is still lacking. In this paper, we initiate the generalization analysis of regularized vector-valued learning algorithms by presenting bounds with a mild dependency on the output dimension and a fast rate on the sample size. Our discussions relax the existing assumptions on the restrictive constraint of hypothesis spaces, smoothness of loss functions and low-noise condition. To understand the interaction between optimization and learning, we further use our results to derive the first generalization bounds for stochastic gradient descent with vector-valued functions. We apply our general results to multi-class classification and multi-label classification, which yield the first bounds with a logarithmic dependency on the output dimension for extreme multi-label classification with the Frobenius regularization. As a byproduct, we derive a Rademacher complexity bound for loss function classes defined in terms of a general strongly convex function.
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