Microblog Sentiment Classification Research Articles

Sentiment classification of microblog: A framework based on BERT and CNN with attention mechanism

BERT, a boon to natural language understanding, extracts the context information of words and forms the basis of the newly-designed sentiment classification framework for Chinese microblogs. Coupled with a CNN and an attention mechanism, the BERT model takes Chinese characters as inputs for vectorization and outputs two kinds of vectors: character-level vectors and sentence-level vectors. The character-level vectors are input into the CNN, and both top-K-average pooling and attention pooling are used to mine emotions from the microblogs. The attention mechanism is used to fuse various features to the microblog vectors. The final classification results are derived via a softmax function over the microblog vectors. The results show an outstanding improvement over benchmark methods on the two classification tasks.

Exploring user historical semantic and sentiment preference for microblog sentiment classification

Microblog text is usually very short, thereby challenging existing sentiment classification methods by providing models with little context. Recently, historical user information has been widely used in many real-world applications, such as recommender systems. However, few research works consider user historical states in the loop of microblog sentiment analysis. In this work, we propose to involve historical user information for microblog sentiment analysis to alleviate the context sparsity problem. In particular, we propose a novel neural microblog sentiment classification method which learns informative representations of microblog posts by exploiting both a user’s contextual information and his/her historical state information. The proposed method consists of four components, i.e., a micropost encoder, a user historical sentiment encoder, a User Historical Semantic Encoder, and a micropost sentiment classification component. Extensive experiments are conducted on real-world data collected from Weibo, and experimental results show that the proposed approach achieves superior performance as compared to state-of-the-art baselines.

Research on sentiment classification of micro-blog short text based on topic clustering

Aiming at the shortage of research on micro-blog short text fine-grained sentiment classification, a fine-grained sentiment classification method about micro-blog short text based on PLSA model and K-means clustering model was proposed. PLSA is used to calculate the probability matrix between documents and topics, words and topics in the corpus. In terms of the probability distribution of words and topics, K-means algorithm is used to cluster the probability distribution of words on topics and merge the similar topics. Based on the sentiment ontology library, emotion recognition is carried out for the merged topics. Then, according to the merged document and topic probability matrix, the document sentiment category is classified. The experimental results show that the sentiment analysis method integrated with PLSA and K-means can obtain higher classification accuracy than the PLSA model method alone.

Collaborative community-specific microblog sentiment analysis via multi-task learning

Microblog sentiment analysis has become a hot research area due to its wide applications. There are some methods utilizing social context, but they only built a global sentiment analysis model, failing to extract personalized expressions. Some personalized methods have been proposed to deal with this problem, but they suffer from data sparseness and inefficiency. Based on personalized sentiment analysis methods, we exploit social context information and capture users’ variable and distinctive expressions at a community level to handle these problems. In particular, we propose a collaborative microblog sentiment analysis approach. In our approach, two classifiers are constructed. One is the global microblog sentiment analysis model which can exploit the sentiment shared by all users. One is the community-specific microblog sentiment analysis model which can extract sentiment influenced by user personalities. In addition, we extract community similarity knowledge and employ it to improve the learning process of the community-specific sentiment model. Moreover, we incorporate social contexts into this model as regularization to encourage the sharing sentiment between connected microblogs. An accelerated algorithm is introduced to solve our model. Experiments on two real datasets show that our model can advance the performance of microblog sentiment classification effectively and outperform state-of-art methods significantly.

Multi-view ensemble learning method for microblog sentiment classification

With the rise of microblog services in recent years, microblog sentiment classification has been widely studied and applied in many fields like public opinion monitoring, commodity evaluation and market forecasting. Ensemble methods have been widely used in the feature construction and classification stages of microblog sentiment classification due to their excellent performance. In feature construction, most researchers use feature concatenation or ensemble methods to combine different features while the fusion of two methods is ignored. In classification, most ensemble classification methods combine classifiers based on majority voting or weighted averaging, and they do not fully consider the differences in the information contained in classifiers. In this paper, a novel multi-view ensemble learning method is proposed to fuse the information contained in different features for better microblog sentiment classification. This method consists of two stages: the local fusion stage and the global fusion stage. In the local fusion stage, the raw features and concatenation features are used to construct basic classifiers, and these basic classifiers are combined into five classifier groups to identify the microblog sentiment among all raw feature information. In the global fusion stage, these classifier groups with a global view are further integrated to make more accurate and comprehensive predictions. Two public microblog benchmark datasets provided by Sina Weibo are used in the experiment, and the experimental results show that our method outperforms other compared methods in identifying the polarities of microblog posts.

Micro‐blog sentiment classification using Doc2vec + SVM model with data purification

As a Chinese version of twitter, micro-blog has been popular for many years. On this platform, a lot of comments are generated explosively every day. These comments contain the public's opinions on various topics, which have wide applications in both academic and industrial fields. In recent years, deep learning and some classification algorithms have been applied to sentiment analysis, and good results have been achieved. However, micro-blog sentiment classification is a challenging task, because micro-blog messages are short and noisy, and contain massive user-invented acronyms and informal words. Unfortunately, most researchers pay more attention to analyse the data after deep learning, but only simply remove the noisy data before using algorithm, so the result of sentiment analysis has reached a bottleneck. Here, the authors first purify the data using varied methods before deep learning, then, the Support Vector Machine (SVM) classification algorithm is applied to sentiment classification of micro-blog using many types of features. Through comparing with the method of simply pre-processing data, the results show that their approach can improve the performance of micro-blog sentiment classification effectively and efficiently.

Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm

Integrated learning can be used to combine weak classifiers in order to improve the effect of emotional classification. Existing methods of emotional classification on micro-blogs seldom consider utilizing integrated learning. Personality can significantly influence user expressions but is seldom accounted for in emotional classification. In this study, a micro-blog emotion classification method is proposed based on a personality and bagging algorithm (PBAL). Introduce text personality analysis and use rule-based personality classification methods to divide five personality types. The micro-blog text is first classified using five personality basic emotion classifiers and a general emotion classifier. A long short-term memory language model is then used to train an emotion classifier for each set, which are then integrated together. Experimental results show that compared with traditional sentiment classifiers, PBAL has higher accuracy and recall. The F value has increased by 9%.

Attention based hierarchical LSTM network for context-aware microblog sentiment classification

Analyzing sentiment polarities of microblog has become a hot research topic for both academic and industrial communities. Most of the existing algorithms regard each microblog as an independent training instance. However, the sentiments embedded in short tweets are usually ambiguous and context-aware. Even a non-sentiment word might convey a clear emotional tendency in certain microblog conversations. In this paper, we regard the microblog conversation as sequence, and develop a Context Attention based Long Short-Term Memory (CA-LSTM) network to incorporate preceding tweets for context-aware sentiment classification. The CA-LSTM network has a hierarchial structure for modeling microblog sequence and allocates the words and tweets with different weights using attention mechanism. Our proposed method can not only alleviate the sparsity problem in feature space, but also capture long distance sentiment context dependency in microblog conversations. Experimental evaluations on a public available dataset show that the proposed CA-LSTM network with context information can outperform other strong baselines by a large margin.

Advanced Sentiment Classification of Tibetan Microblogs on Smart Campuses Based on Multi-Feature Fusion

Sentiment analysis is an important problem in natural language processing, which plays an important role in many fields, such as information forecasting, knowledge classification, and product review. Because Tibetan microblogs have their own unique form, particularly the heterogeneous features, such as the emoticons, the grammatical relations, and the speech, the existing sentiment analysis method has difficulty in analyzing the emotions that such microblogs express. In this paper, we propose a sentiment classification method for Tibetan microblogs based on multi-feature fusion. To better study the affection of affective features, this paper first determines the theme of Weibo texts and chooses smart campuses as theme of Weibo texts for analyzing the influence of each feature on the sentiment of the microblog. Then, these features are fused as a multi-feature, and the sentiment of the Tibetan microblog is classified according to the multi-feature fusion. The experimental results demonstrated that the sentiment classification algorithm based on feature fusion improved the accuracy of microblog sentiment classification.

Chinese micro-blog sentiment classification through a novel hybrid learning model

With the rising and spreading of micro-blog, the sentiment classification of short texts has become a research hotspot. Some methods have been developed in the past decade. However, since the Chinese and English are different in language syntax, semantics and pragmatics, sentiment classification methods that are effective for English twitter may fail on Chinese micro-blog. In addition, the colloquialism and conciseness of short Chinese texts introduces additional challenges to sentiment classification. In this work, a novel hybrid learning model was proposed for sentiment classification of Chinese micro-blogs, which included two stages. In the first stage, emotional scores were calculated over the whole dataset by utilizing an improved Chinese-oriented sentiment dictionary classification method. Data with extremely high or low scores were directly labeled. In the second stage, the remaining data were labeled by using an integrated classification method based on sentiment dictionary, support vector machine (SVM) and k-nearest neighbor (KNN). An improved feature selection method was adopted to enhance the discriminative power of the selected features. The two-stage hybrid framework made the proposed method effective for sentiment classification of Chinese micro-blogs. Experiments on the COAE2014 (Chinese Opinion Analysis Evaluation 2014) dataset show that the proposed method outperforms other schemes.

Research on Chinese Micro-blog Sentiment Classification Based on Recurrent Neural Network

In order to improve the accuracy of Chinese micro-blog sentiment classification, a sentiment classification method based on a kind of improved recurrent neural network model was proposed. Firstly, for the extraction of the text intrinsic semantic features, a feature fusion method based on shallow and deep learning was adopted. Secondly, for the micro-blog text sentiment classification problem, an improved recurrent neural network model was adopted, which replaced the hidden layer of general recurrent neural network into LSTM structure. Finally, the classification accuracy rate was 85.04%, which was 3.17% higher than that of the traditional SVM with shallow learning features. The results confirm the effectiveness of the proposed method.

Microblog sentiment classification with heterogeneous sentiment knowledge

Microblogging services, such as Twitter, are very popular for information release and dissemination. Analyzing the sentiments in massive microblog messages is useful for sensing the public’s opinions on various topics, which has wide applications in both academic and industrial fields. However, microblog sentiment analysis is a challenging task, because microblog messages are short and noisy, and contain massive user-invented acronyms and informal words. It is expensive and time-consuming to manually annotate sufficient samples for training an accurate and robust microblog sentiment classifier. Fortunately, unlabeled microblog messages can provide a lot of useful sentiment knowledge. For example, emoticons are frequently used in microblog messages and they usually indicate sentiment orientations. In this paper, we propose to extract useful sentiment knowledge from massive unlabeled messages to enhance microblog sentiment classification. Three kinds of sentiment knowledge, i.e., contextual similarity knowledge, word-sentiment knowledge, and contextual polarity knowledge, are explored. We propose a unified framework to incorporate the heterogenous sentiment knowledge into the learning of microblog sentiment classifiers. An efficient optimization method based on ADMM is introduced to solve the model of our framework and an accelerated algorithm is proposed to tackle the most time-consuming step. Extensive experiments were conducted on three benchmark Twitter datasets. The experimental results show that our approach can improve the performance of microblog sentiment classification effectively and efficiently.

Study of Sentiment Classification for Chinese Microblog Based on Recurrent Neural Network

The sentiment classification of Chinese Microblog is a meaningful topic. Many studies has been done based on the methods of rule and word-bag, and to understand the structure information of a sentence will be the next target. We proposed a sentiment classification method based on Recurrent neural network (RNN). We adopted the technology of distributed word representation to construct a vector for each word in a sentence; then train sentence vectors with fixed dimension for different length sentences with RNN, so that the sentence vectors contain both word semantic features and word sequence features; at last use softmax regression classifier in the output layer to predict each sentence's sentiment orientation. Experiment results revealed that our method can understand the structure information of negative sentence and double negative sentence and achieve better accuracy. The way of calculating sentence vector can help to learn the deep structure of sentence and will be valuable for different research area.

Personalized Microblog Sentiment Classification via Multi-Task Learning

Microblog sentiment classification is an interesting and important research topic with wide applications. Traditional microblog sentiment classification methods usually use a single model to classify the messages from different users and omit individuality. However, microblogging users frequently embed their personal character, opinion bias and language habits into their messages, and the same word may convey different sentiments in messages posted by different users. In this paper, we propose a personalized approach for microblog sentiment classification. In our approach, each user has a personalized sentiment classifier, which is decomposed into two components, a global one and a user-specific one. Our approach can capture the individual personality and at the same time leverage the common sentiment knowledge shared by all users. The personalized sentiment classifiers of massive users are trained in a collaborative way based on multi-task learning to handle the data sparseness problem. In addition, we incorporate users' social relations into our model to strengthen the learning of the personalized models. Moreover, we propose a distributed optimization algorithm to solve our model in parallel. Experiments on two real-world microblog sentiment datasets validate that our approach can improve microblog sentiment classification accuracy effectively and efficiently.

Structured microblog sentiment classification via social context regularization

Microblog sentiment analysis is a fundamental problem for many interesting applications. Existing microblog sentiment classification methods judge the sentiment polarity mainly according to textual content. However, since microblog messages are very short and noisy, and their sentiment polarities are often ambiguous and context-dependent, the accuracy of microblog sentiment classification is usually unsatisfactory. Fortunately, microblog messages lie in social media and contain rich social contexts. The social context information often implies sentiment connections between microblog messages. For example, a microblogging user usually expresses the same sentiment when posting multiple messages towards the same topic. Motivated by these observations, in this paper we propose a structured microblog sentiment classification (SMSC) framework. Our framework can combine social context information with textual content information to improve microblog sentiment classification accuracy. Two kinds of social contexts are used in our framework, i.e., social connections between microblog messages brought by the same author and social connections brought by social relations between users. In our framework, social context information is formulated as the graph structure over the sentiments of microblog messages. The objective function of our framework is a tradeoff between the agreement with content-based sentiment predictions and the consistency with social contexts. An efficient optimization algorithm is introduced to solve our framework. Experimental results on two Twitter sentiment analysis benchmark datasets indicate that our method can outperform baseline methods consistently and significantly.

Microblog Sentiment Classification with Contextual Knowledge Regularization

Microblog sentiment classification is an important research topic which has wide applications in both academia and industry. Because microblog messages are short, noisy and contain masses of acronyms and informal words, microblog sentiment classification is a very challenging task. Fortunately, collectively the contextual information about these idiosyncratic words provide knowledge about their sentiment orientations. In this paper, we propose to use the microblogs' contextual knowledge mined from a large amount of unlabeled data to help improve microblog sentiment classification. We define two kinds of contextual knowledge: word-word association and word-sentiment association. The contextual knowledge is formulated as regularization terms in supervised learning algorithms. An efficient optimization procedure is proposed to learn the model. Experimental results on benchmark datasets show that our method can consistently and significantly outperform the state-of-the-art methods.

Semi-supervised Micro-blog Sentiment Classification Method Combining Active Learning and Co-training

Semi-supervised Micro-blog Sentiment Classification Method Combining Active Learning and Co-training

Emotional Classification Algorithm of Micro-Blog Text Based on the Combination of Emotional Characteristics

Now micro-blog media is growing fast and micro-blog short text has also become a new type of information carrier. User’s sentiment orientation and emotion of the topic or event in a large number of user’s micro-blog, can not only provide decision-making basis in business but also provide support for government's public opinion monitoring. During micro-blog emotion classification, characteristic information is extracted directly influences the classification effect. This paper uses emotional sentences, emotional symbol, emotional word polarity and other emotional information as classification feature, and use NLP&CC Chinese micro-blog sentiment analysis evaluation standard segmentation of emotion in the polarity based emotion. This paper proposed the Chinese micro-blog sentiment classification based on the feature of amorous feeling. Parallel tests suggested that this method has better classification results, and has verified when micro-blog text’s emotional level is higher, the effect of the method is better.

Research on Sentiment Classification of Chinese Micro Blog Based on Machine Learning

This thesis has conducted an empirical research on sentiment classification of micro blog in three machine learning algorithms, three feature selection algorithms and three feature item weighting algorithms. As the experimental result shows, considering different feature weighting algorithms, SVM and Naive Bayes have their own advantages, and Information Gain (IG) feature selection algorithm is apparently more effective than other methods. Considering the three factors as a whole, it is most effective to have sentiment classification on micro blog by adopting SVM, IG and TF-IDF (Term Frequency-Inverse Document Frequency) as feature items weighting. It has compared the generality of classification model between micro blog comments and ordinary comments in the field of films, and as a result, the experimental results show that the performance of sentiment classification relies on the style of reviews.