Performance Of Text Clustering Research Articles

Internet Rumor Audience Response Prediction Algorithm Based on Machine Learning in Big Data Environment

Rumors are an important factor affecting social stability in some special times. Therefore, the dissemination and prevention and control mechanisms of rumors have always been issues of concern to the academic community and have long been highly valued and widely discussed by experts and scholars. However, in combination with the Internet as a new type of media, although people have begun to pay attention to online rumors, research on it is still relatively fragmented, especially in the cross-domain research specific to the social influence of online rumors, and there is no clear indication of online rumors. The specific definition also did not analyze in detail the internal connection between its influence and group behavior. Therefore, this article will combine actual cases to explore and analyze the spread and influence process of online rumors and show its social influence, hoping to enrich the research of online rumors. Nowadays, the Internet has become the most important carrier to reflect the public grievances. Internet users have expressed their opinions on hot issues such as enterprises, people’s livelihood, and government management, which has formed a powerful public opinion pressure, which has far exceeded the traditional media. The hidden dangers of security cannot be ignored. Therefore, how to monitor network public opinion from a large amount of network data is a difficult problem that needs to be solved urgently. Firstly, this consists of four modules: information collection, web page preprocessing, public opinion analysis, and public information report. Secondly, text clustering, the core technology of network public opinion, is optimized, and single-pass algorithm based on double threshold is proposed. Then the dual-threshold single-pass algorithm is optimized based on the MapReduce parallel computing model, and finally a network public opinion collection technology is formed under the background of big data. Simulation results can greatly improve the performance of text clustering and can effectively optimize the design using the parallel computing model based on MapReduce. The average miss rate after optimization is 0.7569 times, the average false alarm rate is 0.5556 times, and C det is 0.5714 times. It proves that the collection technology based on machine learning under the background of big data is effective and has good performance.

Document Length Variation in the Vector Space Clustering of News in Arabic: A Comparison of Methods

This article is concerned with addressing the effect of document length variation on measuring the semantic similarity in the text clustering of news in Arabic. Despite the development of different approaches for addressing the issue, there is no one strong conclusion recommending one approach. Furthermore, many of these have not been tested for the clustering of news in Arabic. The problem is that different length normalization methods can yield different analyses of the same data set, and that there is no obvious way of selecting the best one. The choice of an inappropriate method, however, has negative impacts on the accuracy and thus the reliability of clustering performance. Given the lack of agreement and disparity of opinions, we set out to comprehensively evaluate the existing normalization techniques to prove empirically which one is the best for the normalization of text length to improve the text clustering performance of news in Arabic. For this purpose, a corpus of 693 stories representing different categories and of different lengths is designed. Data is analyzed using different document length normalization methods along with vector space clustering (VSC), and then the analysis on which the clustering structure agrees most closely with the bibliographic information of the news stories is selected. The analysis of the data indicates that the clustering structure based on the byte length normalization method is the most accurate one. One main problem, however, with this method is that the lexical variables within the data set are not ranked which makes it difficult for retaining only the most distinctive lexical features for generating clustering structures based on semantic similarity. As thus, the study proposes the integration of TF-IDF for ranking the words within all the documents so that only those with the highest TF-IDF values are retained. It can be finally concluded that the proposed model proved effective in improving the function of the byte normalization method and thus on the performance and reliability of news clustering in Arabic. The findings of the study can also be extended to IR applications in Arabic. The proposed model can be usefully used in supporting the performance of the retrieval systems of Arabic in finding the most relevant documents for a given query based on semantic similarity, not document length.

Clustering Chinese Documents Based on HybridDT and LDA Model

In traditional VSM model based text clustering approach, the abstract similarities among documents are represented by the cosine values between the corresponding space vectors. Although it is simple and intuitive, it ignores the semantic and polysemy information of the documents. Aiming to resolve these problems, a clustering framework is proposed in this paper. The framework contains two parts: (1) pre-training text by using Doc2vec to obtain document vector and combining it into TFIDF strategy based weight space vector, which is called HybridDT; (2) using Latent Dirichlet allocation to get the topic distribution and linearly mixing it as feature into HybridDT. The experimental results using the proposed framework on Fudan University Chinese corpus show that the performance of text clustering are improved significantly by combining HybridDT and LDA model.

Cluster Analysis for Internet Public Sentiment in Universities by Combining Methods

A clustering method based on the Latent Dirichlet Allocation and the VSM model to compute the text similarity is presented. The Latent Dirichlet Allocation subject models and the VSM vector space model weights strategy are used respectively to calculate the text similarity. The linear combination of the two results is used to get the text similarity. Then the k-means clustering algorithm is chosen for cluster analysis. It can not only solve the deep semantic information leakage problems of traditional text clustering, but also solve the problem of the LDA that could not distinguish the texts because of too much dimension reduction. So the deep semantic information is mined from the text, and the clustering efficiency is improved. Through the comparisons with the traditional methods, the result shows that this algorithm can improve the performance of text clustering.