Microblog Sentiment Analysis Research Articles

Microblog Sentiment Analysis Based on Paragraph Vectors

Microblog sentiment analysis aims at discovering the users' attitude of hot events. Difficulties of microblog sentiment analysis lie on the short length of text and lack of labeled corpora. Para2vec based on deep learning attracts people's attention recently and the low-dimensional paragraph vectors trained by para2vec get excellent results on sentiment analysis. But when applying it for sentiment analysis on microblogs, we find it does not work so well as on ordinary texts. In this paper, we analyse the weakness of microblog sentiment analysis based on paragraph vectors. And then, we propose two categories of methods, model extension and emotional tendency vectors, to improve the model para2vec. The experimental results confirmed the rationality of our methods. Data analysis shows that our improved methods can effectively reduce the adverse effects of the short text and greatly improve the accuracy of sentiment analysis.

Microblog Sentiment Orientation Detection Using User Interactive Relationship

The development and popularity of microblog have made sentiment analysis of tweets and Weibo an important research field. However, the characteristics of microblog message pose challenge for the sentiment analysis and mining. The existing approaches mostly focus on the message content and context information. In this paper, we propose a novel microblog sentiment analysis framework by incorporating the social interactive relationship factor in the content-based approach. By exploring the interactive relationship on social network based on posted messages, we build social interactive model to represent the opposition or acceptation behavior. Based on the interactive relationship model, the sentiment of microblog message with sparse emotion terms can be deduced and identified, and the sentiment uncertainty can be alleviated to some extent. Afterwards, we transform the classification problem into an optimization problem. Experimental results on Weibo data set indicate that the proposed method can outperform the baseline methods.

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 Analysis with Emoticon Space Model

Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model (ESM) that leverages more emoticons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus (NLP&CC 2013) indicate that ESM effectively leverages emoticon signals and outperforms previous state-of-the-art strategies and benchmark best runs.

Integrated microblog sentiment analysis from users’ social interaction patterns and textual opinions

Traditional post-level opinion classification methods usually fail to capture a person's overall sentiment orientation toward a topic from his/her microblog posts published for a variety of themes related to that topic. One reason for this is that the sentiments connoted in the textual expressions of microblog posts are often obscure. Moreover, a person's opinions are often influenced by his/her social network. This study therefore proposes a new method based on integrated information of microblog users' social interactions and textual opinions to infer the sentiment orientation of a user or the whole group regarding a hot topic. A Social Opinion Graph (SOG) is first constructed as the data model for sentiment analysis of a group of microblog users who share opinions on a topic. This represents their social interactions and opinions. The training phase then uses the SOGs of training sets to construct Sentiment Guiding Matrix (SGM), representing the knowledge about the correlation between users' sentiments, Textual Sentiment Classifier (TSC), and emotion homophily coefficients of the influence of various types of social interaction on users' mutual sentiments. All of these support a high-performance social sentiment analysis procedure based on the relaxation labeling scheme. The experimental results show that the proposed method has better sentiment classification accuracy than the textual classification and other integrated classification methods. In addition, IMSA can reduce pre-annotation overheads and the influence from sampling deviation.

A recommendation mechanism for web publishing based on sentiment analysis of microblog

Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mechanism are extracted by natural language processing including segmentation, lexical analysis and strategy selection. Then, we query the background knowledge base based on linked open data (LOD) with the basic information of users. The experiment result shows that the accuracy of recommendation is within the range of 70% -89% with sentiment analysis and semantic query. Compared with traditional recommendation method, this method can satisfy users’ requirement greatly.

A Hybrid Strategy for Fine-Grained Sentiment of Microblog

Currently, most sentiment analysis of microblog has been focused on coarse-grained sentiment analysis, but fine-grained sentiment is better for reflecting the opinion of the public when they are facing the social focus. Therefore, a hybrid strategy which is a combination of Naive Bayesian and two-layer CRFs is put forward, which has been applied to the finegrained sentiment analysis of Chinese microblog. First, microblog is classified into two types: sentiment and non-sentiment by using Naive Bayesian classification algorithm. And then the first-layer CRFs model is built for the topic emotional sentence. Finally CRFs algorithm is used again to do multi-classification to assign a specific sentiment category. Experimental results show that a good result in sentiment identification based on the combination of Naive Bayesian and CRFs, and also show the advantage of the combination of Naive Bayesian and CRFs interrelated with emotional sentence extraction based on CRFs.

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.

Sentiment Analysis of Chinese Micro Blog Using Machine Learning and an Improved Feature Selection Method

With the rapid development of Internet and occurrence of social media services, many users are becoming the creators of social information. However, the normal manual work can't deal with a large number of subjective messages. As a new kind of social media service, micro blog has been widely accepted and can be used for sentiment analysis. This paper compared performances of three machine learning methods on sentiment analysis of Chinese micro blog. We also proposed an improved feature selection method that increases the accuracy of classification. Experiment results show that SVM is closed to Naïve Bayes and they are better than logistic regression in most cases.

A Method of Micro-Blog Sentiment Analysis Based on PAD Model

This paper presents a new method of micro-blog sentiment analysis, introducing quantifiable PAD emotion model applied to the emotion analysis of the Chinese micro-blog, to get more detailed classifications, and to their quantitative description micro-blog expression of emotion. Using affective analysis wording set of HowNet, we built the PAD emotion dictionary, calculated the emotional orientation of micro-blog based on it. The experiments show that analysis of the micro-blog emotion using the PAD emotion model can get practical accuracy.

Discovering Consumer Insight from Twitter via Sentiment Analysis

Traditional approaches for studying consumer behavior, such as marketing survey and focus group, require a large amount of time and resources. Moreover, some products, such as smartphones, have a short product life cycle. As an alternative solution, we propose a system, the Micro-blog Sentiment Analysis System (MSAS), based on sentiment analysis to automatically analyze customer opinions from the Twitter micro-blog service. The MSAS consists of five main functions to (1) collect Twitter posts, (2) filter for opinionated posts, (3) detect polarity in each post, (4) categorize product features and (5) summarize and visualize the overall results. We used the product domain of smartphone as our case study. The experiments on 100,000 collected posts related to smartphones showed that the system could help indicating the customers' sentiments towards the product features, such as Application, Screen, and Camera. Further evaluation by experts in smartphone industry confirmed that the system yielded some valid results.