Social Emotion Classification Research Articles

Weighted cluster-level social emotion classification across domains

Weighted cluster-level social emotion classification across domains

Social emotion classification of Japanese text information based on SVM and KNN

How to model multi-modal data and rich context information under the same model framework has become the key to user sentiment analysis in social media. This research uses SVM and KNN to build models for Japanese social sentiment classification and proposes a topic model for user sentiment analysis based on SVM and KNN. Moreover, taking TE process data as an example, this paper selects radial basis kernel function and grid search method for the construction of SVM classifier. In addition, by introducing the correlation variable between the sentiment of the comment and the sentiment of the tweet, the comment is related to the original tweet. Based on the establishment of the model, this research proposes a parameter estimation algorithm for model solving based on the idea of SVM-KNN, and uses the Twitter real data set as the experimental data set to verify the effectiveness of the user sentiment analysis model proposed in this paper. The research results show that the method proposed in this paper has a certain effect.

WITHDRAWN: Integrated feature selection (IFS) algorithm and enhanced weight based convolutional neural network (EWCNN) for social emotion classification

Abstract To design and implement IFS-EWCNN for improve the performance of social emotion classification for twitter reviews. Design a new Integrated Feature Selection (IFS) algorithm, which combines filter and wrapper methods procedure for evaluating semantic gap among source features produced using teaching model and target task characteristics before the application of sparse encoding to transfer learning. Filtering algorithm follows the procedure of the information gain and Pearson’s Correlation. In the wrapper selection algorithm follows the procedure of the Expectation Maximization with Forward Search (EMFS), and Exhaustive feature selection. Then implemented a novel Animal Migration Optimization based Sparse Encoding (AMO-SC) for forming dense high-level features by transforming sparse low-level features and further investigation is required on effectiveness of this method on emotion classification. Finally, for classifications of emotions, proposed an Enhanced Weight based Convolutional Neural Network (EWCNN). It concludes that proposed IFS-EWCNN classifier achieves better performance compared with the existing methods in terms of f-measure, specificity, sensitivity, recall, precision and accuracy. The experiments are carried out in SemEval 2016 Task 4A for sentiment and SemEval 2018 Task 1C emotion classification using Matlab tool. The proposed IFS-EWCNN classifier approaches accurately classify the social emotions.

Enhanced Weight Based Convolutional Neural Network (EWCNN) and Fuzzy Clustering For Semantically Rich Multi-Label Social Emotion Classification

In Recent Years, Social Emotion In Recent Years Acquires Natural Language Processing Researchers’ Attention, Because Of Analyzing User-Generated Emotional Documents On The Web. But, These Emotions Has Noisy Instance Mixed And It Is Great Dispute To Acquire The Textual Meaning Of Short Messages. Definition: In General, Large-Scale Datasets Will Have Many Noisy Data, Which Can’t Be Used Readily And Also It Is Costly, Because Of Ambiguity Of Various Informal Expressions In User-Generated Comments. It Is Very Tedious One To Recognize The Similar User Documents From The Entire Social Media Text Message. Furthermore, Online Comments Are Characteristically Categorized By A Sparse Feature Space, Which Makes The Respective Emotion Classification Task A Complex One. Methodology: Three Major Contributions Were Done In This Work In Order To Rectify These Problems, They Are: Development Of A Novel Mutation Bat Optimization Based Sparse Encoding (MBO-SC) Which Transforming The Sparse Low-Level Features Into Dense High-Level Features, Was The 1st Contribution, Next Is, An Enhanced Weight Based Convolutional Neural Network (EWCNN) To Target-Specific Layer. It Influences The Semantically EWCNN Classifier To Include Semantic Domain Knowledge Into The Neural Network To Bootstrap Its Inference Power And Interpretability. Fuzzy Clustering Algorithm Is Proposed To Minimize The Similarity Among Two Documents. Uses: It Is Quite Constructive In Recommending Products, Collecting Public Opinions, And Predicting Election Results. Proposed Work Is Distinguished With The Existing Methods, With The Metrics Such As: Precision, Recall, Sensitivity, Specificity, F-Measure And Accuracy. From The Experimental Result It Is Confirmed That The Quality Of Learned Semantic Vectors And The Performance Of Social Emotion Classification Can Be Enhanced By Proposed Models.

Tree-Structured Neural Networks With Topic Attention for Social Emotion Classification

Social emotion classification studies the emotion distribution evoked by an article among numerous readers. Although recently neural network-based methods can improve the classification performance compared with the previous word-emotion and topic-emotion approaches, they have not fully utilized some important sentence language features and document topic features. In this paper, we propose a new neural network architecture exploiting both the syntactic information of a sentence and topic distribution of a document. The proposed architecture first constructs a tree-structured long short-term memory (Tree-LSTM) network based on the sentence syntactic dependency tree to obtain a sentence vector representation. For a multi-sentence document, we then use a Chain-LSTM network to obtain the document representation from its sentences’ hidden states. Furthermore, we design a topic-based attention mechanism with two attention levels. The word-level attention is used for weighting words of a single-sentence document and the sentence-level attention for weighting sentences of a multi-sentence document. The experiments on three public datasets show that the proposed scheme outperforms the state-of-the-art ones in terms of higher average Pearson correlation coefficient and MicroF1 performance.

Ensemble learning on visual and textual data for social image emotion classification

Texts, images and other information are posted everyday on the social network and provides a large amount of multimodal data. The aim of this work is to investigate if combining and integrating both visual and textual data permits to identify emotions elicited by an image. We focus on image emotion classification within eight emotion categories: amusement, awe, contentment, excitement, anger, disgust, fear and sadness. Within this classification task we here propose to adopt ensemble learning approaches based on the Bayesian model averaging method, that combine five state-of-the-art classifiers. The proposed ensemble approaches consider predictions given by several classification models, based on visual and textual data, through respectively a late and an early fusion schemes. Our investigations show that an ensemble method based on a late fusion of unimodal classifiers permits to achieve high classification performance within all of the eight emotion classes. The improvement is higher when deep image representations are adopted as visual features, compared with hand-crafted ones.

Bootstrapping Social Emotion Classification with Semantically Rich Hybrid Neural Networks

Social emotion classification aims to predict the aggregation of emotional responses embedded in online comments contributed by various users. Such a task is inherently challenging because extracting relevant semantics from free texts is a classical research problem. Moreover, online comments are typically characterized by a sparse feature space, which makes the corresponding emotion classification task very difficult. On the other hand, though deep neural networks have been shown to be effective for speech recognition and image analysis tasks because of their capabilities of transforming sparse low-level features to dense high-level features, their effectiveness on emotion classification requires further investigation. The main contribution of our work reported in this paper is the development of a novel model of semantically rich hybrid neural network (HNN) which leverages unsupervised teaching models to incorporate semantic domain knowledge into the neural network to bootstrap its inference power and interpretability. To our best knowledge, this is the first successful work of incorporating semantics into neural networks to enhance social emotion classification and network interpretability. Through empirical studies based on three real-world social media datasets, our experimental results confirm that the proposed hybrid neural networks outperform other state-of-the-art emotion classification methods.

Social emotion classification based on noise-aware training

Social emotion classification draws many natural language processing researchers’ attention in recent years, since analyzing user-generated emotional documents on the Web is quite useful in recommending products, gathering public opinions, and predicting election results. However, the documents that evoke prominent social emotions are usually mixed with noisy instances, and it is also challenging to capture the textual meaning of short messages. In this work, we focus on reducing the impact of noisy instances and learning a better representation of sentences. For the former, we introduce an “emotional concentration” indicator, which is derived from emotional ratings to weight documents. For the latter, we propose a new architecture named PCNN, which utilizes two cascading convolutional layers to model the word-phrase relation and the phrase–sentence relation. This model regards continuous tokens as phrases based on an assumption that neighboring words are very likely to have internal relations, and semantic feature vectors are generated based on the phrase representation. We also present a Bayesian-based model named WMCM to learn document-level semantic features. Both PCNN and WMCM classify social emotions by capturing semantic regularities in language. Experiments on two real-world datasets indicate that the quality of learned semantic vectors and the performance of social emotion classification can be improved by our models.

Multi-label maximum entropy model for social emotion classification over short text

Social media provides an opportunity for many individuals to express their emotions online. Automatically classifying user emotions can help us understand the preferences of the general public, which has a number of useful applications, including sentiment retrieval and opinion summarization. Short text is prevalent on the Web, especially in tweets, questions, and news headlines. Most of the existing social emotion classification models focus on the detection of user emotions conveyed by long documents. In this paper, we introduce a multi-label maximum entropy (MME) model for user emotion classification over short text. MME generates rich features by modeling multiple emotion labels and valence scored by numerous users jointly. To improve the robustness of the method on varied-scale corpora, we further develop a co-training algorithm for MME and use the L-BFGS algorithm for the generalized MME model. Experiments on real-world short text collections validate the effectiveness of these methods on social emotion classification over sparse features. We also demonstrate the application of generated lexicons in identifying entities and behaviors that convey different social emotions.

Social emotion classification of short text via topic-level maximum entropy model

With the rapid proliferation of Web 2.0, the identification of emotions embedded in user-contributed comments at the social web is both valuable and essential. By exploiting large volumes of sentimental text, we can extract user preferences to enhance sales, develop marketing strategies, and optimize supply chain for electronic commerce. Pieces of information in the social web are usually short, such as tweets, questions, instant messages, messages, and news headlines. Short text differs from normal text because of its sparse word co-occurrence patterns, which hampers efforts to apply social emotion classification models. Most existing methods focus on either exploiting the social emotions of individual words or the association of social emotions with latent topics learned from normal documents. In this paper, we propose a topic-level maximum entropy (TME) model for social emotion classification over short text. TME generates topic-level features by modeling latent topics, multiple emotion labels, and valence scored by numerous readers jointly. The overfitting problem in the maximum entropy principle is also alleviated by mapping the features to the concept space. An experiment on real-world short documents validates the effectiveness of TME on social emotion classification over sparse words.

Social Emotion Classification via Reader Perspective Weighted Model

With the development of Web 2.0, many users express their opinions online. This paper is concerned with the classification of social emotions on varied-scale datasets. Different from traditional models which weight training documents equally, the concept of emotional entropy is proposed to estimate the weight and tackle the issue of noisy documents. The topic assignment is also used to distinguish different emotional senses of the same word. Experimental evaluations using different data sets validate the effectiveness of the proposed social emotion classification model.

Contextual Sentiment Topic Model for Adaptive Social Emotion Classification

Social emotion classification is important for numerous applications, such as public opinion measurement, corporate reputation estimation, and customer preference analysis. However, topics that evoke a certain emotion in the general public are often context-sensitive, making it difficult to train a universal classifier for all collections. A multilabeled sentiment topic model, namely, the contextual sentiment topic model (CSTM), can be used for adaptive social emotion classification. The CSTM distinguishes context-independent topics from both a background theme, which characterizes nondiscriminative information, and a contextual theme, which characterizes context-dependent information across different collections. Experimental results demonstrated the effectiveness of this model for the adaptive classification of social emotions.

Sentiment topic models for social emotion mining

The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs/tweets, instant-messages, and so forth. This article concentrates on the mining of readers’ emotions evoked by social media materials. Compared to the classical sentiment analysis from writers’ perspective, sentiment analysis of readers is sometimes more meaningful in social media. We propose two sentiment topic models to associate latent topics with evoked emotions of readers. The first model which is an extension of the existing Supervised Topic Model, generates a set of topics from words firstly, followed by sampling emotions from each topic. The second model generates topics from social emotions directly. Both models can be applied to social emotion classification and generate social emotion lexicons. Evaluation on social emotion classification verifies the effectiveness of the proposed models. The generated social emotion lexicon samples further show that our models can discover meaningful latent topics exhibiting emotion focus.