Personalized sentiment classification based on latent individuality of microblog users

Sentiment expression in microblog posts often re-flects user’s specific individuality due to different language habit, personal character, opinion bias and so on. Existing sentiment classification algo-rithms largely ignore such latent personal distinc-tions among different microblog users. Meanwhile, sentiment data of microblogs are sparse for indi-vidual users, making it infeasible to learn effective personalized classifier. In this paper, we propose a novel, extensible personalized sentiment classi-fication method based on a variant of latent fac-tor model to capture personal sentiment variations by mapping users and posts into a low-dimensional factor space. We alleviate the sparsity of personal texts by decomposing the posts into words which are further represented by the weighted sentiment and topic units based on a set of syntactic units of words obtained from dependency parsing results. To strengthen the representation of users, we lever-age users following relation to consolidate the in-dividuality of a user fused from other users with similar interests. Results on real-world microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins.

Text segmentation techniques: A critical review

Text segmentation is widely used for processing text. It is a method of splitting a document into smaller parts, which is usually called segments. Each segment has its relevant meaning. Those segments categorized as word, sentence, topic, phrase or any information unit depending on the task of the text analysis. This study presents various reasons of usage of text segmentation for different analyzing approaches. We categorized the types of documents and languages used. The main contribution of this study includes a summarization of 50 research papers and an illustration of past decade (January 2007- January 2017)’s of research that applied text segmentation as their main approach for analysing text. Results revealed the popularity of using text segmentation in different languages. Besides that, the “word” seems to be the most practical and usable segment, as it is the smaller unit than the phrase, sentence or line

Featuring, Detecting, and Visualizing Human Sentiment in Chinese Micro-Blog

2015-2016 > Academic research: refereed > Publication in refereed journa

Automated Social Text Annotation With Joint Multilabel Attention Networks

Automated social text annotation is the task of suggesting a set of tags for shared documents on social media platforms. The automated annotation process can reduce users' cognitive overhead in tagging and improve tag management for better search, browsing, and recommendation of documents. It can be formulated as a multilabel classification problem. We propose a novel deep learning-based method for this problem and design an attention-based neural network with semantic-based regularization, which can mimic users' reading and annotation behavior to formulate better document representation, leveraging the semantic relations among labels. The network separately models the title and the content of each document and injects an explicit, title-guided attention mechanism into each sentence. To exploit the correlation among labels, we propose two semantic-based loss regularizers, i.e., similarity and subsumption, which enforce the output of the network to conform to label semantics. The model with the semantic-based loss regularizers is referred to as the joint multilabel attention network (JMAN). We conducted a comprehensive evaluation study and compared JMAN to the state-of-the-art baseline models, using four large, real-world social media data sets. In terms of F 1 , JMAN significantly outperformed bidirectional gated recurrent unit (Bi-GRU) relatively by around 12.8%-78.6% and the hierarchical attention network (HAN) by around 3.9%-23.8%. The JMAN model demonstrates advantages in convergence and training speed. Further improvement of performance was observed against latent Dirichlet allocation (LDA) and support vector machine (SVM). When applying the semantic-based loss regularizers, the performance of HAN and Bi-GRU in terms of F 1 was also boosted. It is also found that dynamic update of the label semantic matrices (JMAN d ) has the potential to further improve the performance of JMAN but at the cost of substantial memory and warrants further study

Blog Style Classification: Refining Affective Blogs

In the constantly growing blogosphere with no restrictions on form or topic, a number of writing styles and genres have emerged. Recognition and classification of these styles has become significant for information processing with an aim to improve blog search or sentiment mining. One of the main issues in this field is detection of informative and affective articles. However, such differentiation does not suffice today. In this paper we extend the differentiation and suggest a fine-grained set of subcategories for affective articles. We propose and evaluate a classification method employing novel lexical, morphological, lightweight syntactic and structural features of written text. The results show that our method outperforms the existing approaches