Micro-blog Short Text Research Articles

Emotion Recognition and Analysis Of Netizens Based On Micro-Blog During Covid-19 Epidemic

The research is about emotion recognition and analysis based on Micro-blog short text. Emotion recognition is an important field of text classification in Natural Language Processing. The data of this research comes from Micro-blog 100K record related to COVID-19 theme collected by Data fountain platform, the data are manually labeled, and the emotional tendencies of the text are negative, positive and neutral. The empirical part adopts dictionary emotion recognition method and machine learning emotion recognition respectively. The algorithms used include support vector machine and naive Bayes based on TFIDF, support vector machine and LSTM based on wod2vec. The five results are compared. Combined with statistical analysis methods, the emotions of netizens in the early stage of the epidemic are analyzed for public opinion. This research uses machine learning algorithm combined with statistical analysis to analyze current events in real time. It will be of great significance for the introduction and implementation of national policies.

CONSTRUCTION OF INDIVIDUAL COGNITIVE MODEL IN THE CONTEXT OF EMOTION AND AFFECTIVE DISORDER

CONSTRUCTION OF INDIVIDUAL COGNITIVE MODEL IN THE CONTEXT OF EMOTION AND AFFECTIVE DISORDER

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.

Research on Evaluation Method of LDA Topic Model in Mail Classification

The LDA topic model is a document generation model, which is an unsupervised machine learning technique that can be used in keyword extraction, topic classification, and so on. The main purpose of this paper is to effectively evaluate the number of optimal topics of the LDA topic model in the message subject classification and extract the importance of the topic, so that the LDA topic model can be highly readable after subject classification. Therefore, this paper proposes a keyword matching and subjective statistical value word comparison (KM-SSVW) for the subject classification of emails, the keyword matching technique in this method uses the TF-IDF technique. This method can accurately evaluate the extracted keywords and optimize the number of topics. The data in this article is mainly from the mail gate, a total of 7,000 messages that Hillary communicates with others. The empirical results show that the proposed method of keyword matching and subjective statistical value word comparison has a good effect on subject quantity optimization and subject word readable evaluation. However, there are still some limitations in this paper. In the experiment, new methods are not validated for other types of data sets, such as microblog short text, XML documents, and WeChat public platform articles.

A Chinese unknown word recognition method for micro-blog short text based on improved FP-growth

Unknown word recognition is one of the important research contents of natural language processing. However, there are still problems such as sparse data, corpus noise, and various forms of expressions for the identification of micro-blog short words. This paper proposes an unknown words recognition method POS-FP (Frequent Pattern growth with part- of-speech)for micro-blog short text. Firstly, the candidate unknown words are obtained by combing the N-grams model and frequent item sets. Then the unknown word is filtered and verified by the improved mutual information, information entropy and context dependence. Finally, the open verification method is used to obtain final unknown word. Experiments show that the algorithm improved the unknown word recognition for micro-blog short texts.

Improvement of LDA Algorithm Based on Microblog Short Text Hotspot Analysis

In view of the high sparsity of microblog short text, the simple and random text content and the number of typos, and the use of standard LDA algorithm to calculate the result deviation of the keyword of short text in microblog is insufficient. In this paper, an improved LH-LDA model of microblog short text based on LDA algorithm is proposed. The model simultaneously links the heat of short text labels and short text. Through the relationship between the two and the text content, the keywords of a large amount of text are mined. At the same time, the correctness of the model is derived by Gibbs sampling, and the keywords of Weibo content are obtained. This article was adopted Python web crawler microblogging content, comment, forwarding, and praise. The results show that the traditional LDA algorithm is subject to more interference factors and the processing results are not accurate enough. From the results of the improved LH-LDA model processing in this short text, the keywords of this type of short text can be better found.

Topic Analysis of Microblog About “Didi Taxi” Based on K-means Algorithm

In the age of information and digitization, most users publish and obtain real-time information by microblog in social networks. Through effective means, we can accurately discover, organize, and utilize the valuable information hidden behind the massive short texts of social networks. Then we can explore hot topics in microblog, which is conducive to public opinion monitoring and marketing development. In today's society, Didi Taxi has become a necessary choice for many users to travel. This paper applied K-means clustering algorithm to topic analysis of Sina microblog short text on Didi Taxi. We crawled 17226 search results of microblog relevant to the topic of Didi Taxi from April 2019 to June 2019. After a series of data cleaning and data preprocessing steps, we used TF-IDF method to represent 15054 pieces of text data after processing. Through the evaluation of silhouette coefficient, we set the dimension of text 300 and the number of clusters 34 with K-means. Next, we extracted 8 topic clusters from 34 clusters, which include the advantages and disadvantages of Didi Taxi and its development status. Finally, we discussed the results by human check in semantic perspective. Through the topic analysis of microblog, we can understand the public’s attitude to Didi Taxi and provide the basis for the management of the government or company in the future.

Multi-classification analysis of large data based on knowledge element in micro-blog short text

ABSTRACT To solve the problem that the traditional emotion analysis method for microblog information adopting vocabulary target identification is unsatisfactory for identifying concrete concept application, a recommended system mixing behavioral habits of users and microblog user labels is proposed. Firstly, the content-based method is used to build a statistic model for emotion analysis of microblog information and experimental analysis was carried out for cluster coefficients of emotion analysis so as to acquire the best selected value of cluster coefficients; secondly, the decision classifier is used to build an emotion-based concrete method model for microblog information so as to realize the highest similar mode classification of microblog information and also use content and the method model to rectify it; finally, the performance advantage of the proposed method is verified in concrete statistic analysis through experiments.

The Social and Conceptual Semantic Extended Search Method for Microblog Short Text

The Social and Conceptual Semantic Extended Search Method for Microblog Short Text

Extended search method based on a semantic hashtag graph combining social and conceptual information

Searching for microblog short text by their meaning is a challenging task because of the semantic sparsity of the information in social networks. The extended search approaches are commonly accepted which facilitate short text understanding and search by enriching the short text. However, they only analyze the literal semantics of short text, and the unique social characteristics of social network which also contain semantic information are not utilized well. To better capture the rich semantics in microblog short text, we propose a new microblog short text extended search method based on a semantic hashtag graph by combining social and conceptual information, which enriches each short text by concepts and associated hashtags to represent whole semantic features. Considering the microblog context, we introduce concepts through Wikipedia, as well as semantic consistency of hashtags. Specifically, for conceptual semantics, we propose a conceptual analysis method which merges explicit and implicit information in Wikipedia. For social semantics in hashtags, a semantic hashtag graph which combines social and conceptual information is put forward to generate semantic associated hashtags. We conduct experiments and the results show that our method is obviously better than the other existing state-of-the-art approaches in semantic understanding and search of short text.

Review of intelligent microblog short text processing

This article summarizes the research methods for processing short texts in the microblog, one of the most popular social media sites, analyzes the bottleneck problems and challenging issues, and discusses the possible direction and strategies for solving the above problems. Comprehensive literature review on short text processing has also been conducted by the way of case tracking and case studies. Text categorization and text clustering are the main research methods for processing short texts in microblogs, and topic detection, sentiment analysis, smart healthcare, business intelligence and location services are hot topics for the application of these methods. We also found some challenges for processing microblog short texts, including semantic processing, noise reduction, clarification of fuzzy themes and handling big data. The real-time online processing of big data is expected to be the main research direction in the future.

Micro-blog topic detection method based on BTM topic model and K-means clustering algorithm

The development of micro-blog, generating large-scale short texts, provides people with convenient communication. In the meantime, discovering topics from short texts genuinely becomes an intractable problem. It was hard for traditional topic model-to-model short texts, such as probabilistic latent semantic analysis (PLSA) and Latent Dirichlet Allocation (LDA). They suffered from the severe data sparsity when disposed short texts. Moreover, K-means clustering algorithm can make topics discriminative when datasets is intensive and the difference among topic documents is distinct. In this paper, BTM topic model is employed to process short texts–micro-blog data for alleviating the problem of sparsity. At the same time, we integrating K-means clustering algorithm into BTM (Biterm Topic Model) for topics discovery further. The results of experiments on Sina micro-blog short text collections demonstrate that our method can discover topics effectively.

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.

MicroBlog Orientation Analysis Based on Sentiment Symbol

As a new social networking media, Micro-blog is recognized by more and more people gradually, the orientation analysis of Micro-blog short text messages has become a hotspot in the field of data mining. At present, the research of Micro-blog orientation analysis mainly concentrate in the text, without considering the impact of other emotional factors. By analyzing and studying Sina Micro-blog, this paper added new words and emoticons in the basis of traditional emotional dictionary to construct special Micro-blog dictionary. Meanwhile, it analyzed rhetoric and sentence to improve the effect of orientation analysis. The experimental results showed that the method in Micro-blog orientation analysis obtained better performance.