Microblog Text Research Articles

Efficient temporal mining of micro-blog texts and its application to event discovery

In this paper we present a novel method for clustering words in micro-blogs, based on the similarity of the related temporal series. Our technique, named SAX*, uses the Symbolic Aggregate ApproXimation algorithm to discretize the temporal series of terms into a small set of levels, leading to a string for each. We then define a subset of "interesting" strings, i.e. those representing patterns of collective attention. Sliding temporal windows are used to detect co-occurring clusters of tokens with the same or similar string. To assess the performance of the method we first tune the model parameters on a 2-month 1 % Twitter stream, during which a number of world-wide events of differing type and duration (sports, politics, disasters, health, and celebrities) occurred. Then, we evaluate the quality of all discovered events in a 1-year stream, "googling" with the most frequent cluster n-grams and manually assessing how many clusters correspond to published news in the same temporal slot. Finally, we perform a complexity evaluation and we compare SAX* with three alternative methods for event discovery. Our evaluation shows that SAX* is at least one order of magnitude less complex than other temporal and non-temporal approaches to micro-blog clustering.

Disambiguating company names in microblog text using clustering for online reputation management

Resumen en: Twitter is used by millions of users to publish brief messages (tweets) with the purpose of sharing experiences and/or opinions about a product or servic...

A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management

In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data (sensor data, hydrological data and digital elevation models). We apply this approach to examine the micro-blogging text messages of the Twitter platform (tweets) produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near (up to 10 km) to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring.

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.

Data Detection, Tracking and Sentiment Analysis Based on Micro-Blog Data

With development of information technology and network technique, as information media micro-blog becomes more and more important. Micro-blog is noted for preeminent simple, convenient and interactivity. However with the help of micro-blog, fake information is rampant increasingly. The public opinion analysis of micro-blog data allows of no delay. This paper explicates the features of micro-blog text, and then describes text information extraction technology such as top detection, tracking in detail. The outcomes of information extraction can inform government department spot of internet public opinion in real time.

Multi-modal microblog classification via multi-task learning

Recent years have witnessed the flourishing of social media platforms (SMPs), such as Twitter, Facebook, and Sina Weibo. The rapid development of these SMPs has resulted in increasingly large scale multimedia data, which has been proved with remarkable marketing values. It is in an urgent need to classify these social media data into a specified list of concerned entities, such as brands, products, and events, to analyze their sales, popularity or influences. But this is a rather challenging task due to the shortness, conversationality, the incompatibility between images and text, and the data diversity of microblogs. In this paper, we present a multi-modal microblog classification method in a multi-task learning framework. Firstly features of different modalities are extracted for each microblog. Specifically, we extract TF-IDF features for each microblog text and low-level visual features and high-level semantic features for each microblog image. Then multiple related classification tasks are learned simultaneously for each feature to increase the sample size for each task and improve the prediction performance. Finally the outputs of each feature are integrated by a Support Vector Machine that learns how to optimally combine and weight each feature. We evaluate the proposed method on Brand-Social-Net to classify the contained 100 brands. Experimental results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.

Empirical Study on the Marketing Strategy and Effect on the Platform of Social Media: Taking Tencent Microblog as Example

Microblog is one of the most important trends in Internet communication. But since microblog is a new phenomenon, we are still at the very beginning stage of exploring the ways and effects of marketing on microblog. The paper took Tencent enterprise microblog platform as example, using experiment to discover what kind of microblog text can effectively influence users’ attitudes and behaviors. It was found that advertising information can not significantly changed users’ attitudes toward the brand, but non-advertising information can significantly change users’ attitudes toward the company’s microblog, and ultimately change their attitudes toward brand. Some suggestions for business marketing on microblog were also proposed.

Emotion Classification in Microblog Texts Using Class Sequential Rules

This paper studies the problem of emotion classification in microblog texts. Given a microblog text which consists of several sentences, we classify its emotion as anger, disgust, fear, happiness, like, sadness or surprise if available. Existing methods can be categorized as lexicon based methods or machine learning based methods. However, due to some intrinsic characteristics of the microblog texts, previous studies using these methods always get unsatisfactory results. This paper introduces a novel approach based on class sequential rules for emotion classification of microblog texts. The approach first obtains two potential emotion labels for each sentence in a microblog text by using an emotion lexicon and a machine learning approach respectively, and regards each microblog text as a data sequence. It then mines class sequential rules from the dataset and finally derives new features from the mined rules for emotion classification of microblog texts. Experimental results on a Chinese benchmark dataset show the superior performance of the proposed approach.

The Optimization Algorithm Based on Vector Space Model to Determine the Influence of Microblogging Text

This article uses the vector space model to express microblogging text in quantity, via the cosine of the angle of text clustering method to calculate the similarity between the text. We can generate alternative microblogging hot cluster by centralized method, by adding the "microblogging impact Factor " into vector text mold of alternative hotspot clusters. In order to simulate the exclusive advantage of "star" in the information dissemination process of the real world, we use the alternative hotspot clusters which already weighted the "microblogging impact factor" to re-evaluate its characteristics of vector, to optimize the hot microblogging discovery algorithm and to make the sorting results more in line with the law of information dissemination, finally to give some suggestions and opinions on hot microblogging evaluation method.

A Rule-Based Approach to Sentiment Classification of Chinese Microblogging Texts

Chinese microblogging texts are always short and casual, which bring some troubles to the traditional sentiment classification methods based on learning. To overcome this problem, we use a rule-based approach to classify the sentiment of Chinese microblogging texts. According to the characteristics of Chinese microblogging texts, we construct a thesaurus of subjective words for it, summarize the basic semantic rules expressing emotion and propose a rule-based approach to sentiment classification of Chinese microblogging texts. Finally, we compare our approach with a SVM-based approach. Our rule-based approach achieves an accuracy of 0.865, which is better than that of SVM-based approach.

Research Based on the HowNet Semantic Expansion Micro-Blog Hot Topic Detection System

In this paper, we propose an improved topic detection method based on the HowNet semantic expansion. Using the HowNet to extent the VSM of micro-blog content, solve the problem that the similarity calculation is not accurate because of the length of the micro-blog text is too small and the gap of user comments number is too large through the two-step above. The similarity calculated accurately, would there be a good clustering effect. Put forward a more complete topic heat assessment methods that take into account the number of fans of the Micro-Bo users, the number of forwarding Micro-Bo text, comments, and time factors.

The Chinese Microblog Emotional Tendency Analysis Based on Sentiment Dictionary

With the Web 2.0 development, microblog has become the current hot spots.To HowNet Sentiment dictionary-based, and the current dictionary of commonly used network terminology adds microblog emotional, Sina API access to data, to build the microblog sentiment dictionary, for the Chinese microblog emotional information analysis, the experimental results show that the analytical methods microblog text emotional tendency analysis with good results.

Chinese Microblog Topic Detection Based on the Latent Semantic Analysis and Structural Property

Traditional topic detection method can not be applied to the microblog topic detection directly, because the microblog text is a kind of the short, fractional and grass-roots text. In order to detect the hot topic in the microblog text effectively, we propose a microblog topic detection method based on the combination of the latent semantic analysis and the structural property. According to the dialogic property of the microblog, our proposed method firstly creates semantic space based on the replies to the thread, with the aim to solve the data sparseness problem; secondly, create the microblog model based on the latent semantic analysis; finally, propose a semantic computation method combined with the time information. We then adopt the agglomerative hierarchical clustering method as the microblog topic detection method. Experimental results show that our proposed methods improve the performances of the microblog topic detection greatly.

Extracting Hot Topics from Microblogging Based on Keywords Detection and Text Clustering

Following with news and forums, microblogging becomes the third largest source of Internet public opinion. So it is necessary to do research of microblogging topic discovery. Firstly, we detect hot topics through the the keywords detection algorithm. Secondly, elect most popular microblogging text in the massive microblogging text by combining of keywords weigh and textual information entropy. Finally, using the dynamic clustering algorithm, the microblogging text elected, will form into different news topics by clustering polymerization. According to experimental validation of the true microblogging data, hot topic can be effectively detected from the text of a large number through the method.

Mining spatio-temporal information on microblogging streams using a density-based online clustering method

Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular microblogging services such as Twitter did provide information of top trending topics for selection, it is still unable to fully support users for picking up all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to investigate how microblogging social networks (i.e. Twitter) can be used as a reliable information source of emerging events by extracting their spatio-temporal features from the messages to enhance event awareness. In this work, we applied a density-based online clustering method for mining microblogging text streams, in order to obtain temporal and geospatial features of real-world events. By analyzing the events detected by our system, the temporal and spatial impacts of the emerging events can be estimated, for achieving the goals of situational awareness and risk management.

A public opinion classification algorithm based on micro-blog text sentiment intensity: Design and implementation

on the features of short content and nearly real- time broadcasting velocity of micro-blog information, our lab constructed a public opinion corpus named MPO Corpus. Then, based on the analysis of the status of the network public opinion, it proposes an approach to calculate the sentiment intensity from three levels on words, sentences and documents respectively in this paper. Furthermore, on the basis of the MPO Corpus and HowNet Knowledge-base and sentiment analysis set, the feature words' semantic information is brought into the traditional vector space model to represent micro-blog documents. At the same time, the documents are classified by the subjects and sentiment intensity. Therefore, the experiment result indicates that the proposed method improves the efficiency and accuracy of the micro-blog content classification, the public opinion characteristics analysis and supervision in this paper. Thus, it provides a better technical support for content auditing and public opinion monitoring for micro-blog platform.