Public Opinion Classification Research Articles

Klasifikasi Opini Publik di Twitter Terhadap Bakal Calon Presiden Indonesia Tahun 2024 Menggunakan LSTM Secara Realtime Berbasis Website

The analysis of public opinions from Indonesian netizens regarding the potential presidential candidates for Indonesia in 2024 on Twitter is challenging. Human-based classification of the candidates on Twitter has limitations as it requires expertise and a considerable amount of time to process the data. Therefore, a system that provides realtime visualization of public opinion classification is necessary. Previous research only focused on model evaluation, while this study aims to implement the best model on a website. The objective of this research is to develop a system for monitoring the Twitter-based public opinion classification of the potential presidential candidates for Indonesia in 2024 within specific time frames. The training process utilizes the LSTM method, resulting in a model with an accuracy of 76%. Parameters such as batch size, dropout, and learning rate were tested. The data used in this study was obtained by crawling Twitter using the keywords Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto. The LSTM model was then implemented in a website-based system that generates a dashboard with features such as a color-coded map displaying the highest levels of positive sentiment for each candidate in each province, the overall classification count for each candidate, and filters for sentiment classification based on province and specific time frames.

Public opinion classification and text alignment based on Chinese and Tibetan corpus

To address the need for researching the security of Chinese and Tibetan networks, the classification of public opinion of Chinese and Tibetan texts is proposed. First, web pages are collected. Second, preprocessing is conducted to extract the useful information from web pages. Third, a table of the Chinese and Tibetan public opinion key words is built. Finally, text similarity calculation is proposed to classify the text according to the table of public opinion words. A Chinese–Tibetan text-level alignment approach that is based on Chinese and Tibetan translation dictionary is proposed to match word frequency and position. Furthermore, sentence-level alignment algorithm is studied. The alignment performance is related to the Chinese and Tibetan translation dictionary. Text classification of public opinion and Chinese–Tibetan text alignment system is developed. After public opinion classification of Chinese text, the alignment software can discover the most similar Tibetan text and present it to the user. This research can effectively contribute to identifying Chinese and Tibetan public opinion text and is meaningful for information retrieval, text clustering, and Chinese and Tibetan machine translation.

Research on Tibetan hot words, sensitive words tracking and public opinion classification

The rapid development of Tibetan information technology provides rich resources for Tibetan information processing technology. The construction of Tibetan corpus is the field of Tibetan information processing of basic work. In this paper, we design the system of Tibetan network data collection and web pages preprocessing. It can timely and efficiently access to web resources, and provide a basis for further analysis of Tibetan data. It can establish the Tibetan related corpus, enrich the Tibetan digital resources. It can also alleviate the status of Tibetan corpus data sparse and lack of resources and bring the convenient condition for Tibetan information processing. The hot words reflect the hot spot of Tibetan people’s attention in a certain period of time. Firstly, the paper proposes the method for reducing the space dimension of Tibetan news text. It can effectively reduce the complexity of subsequent processing. Secondly, term weighting method is proposed based on improved TFIDF for Tibetan text information extraction. It utilizes the idea that the words of different locations are given different weights to extract the hot words. On sensitive words discovery and classification of public opinion, sensitive thesaurus are collected artificially. Through the sensitive thesaurus comparison, the sensitive words are extracted. Classification of public opinion words is based on the proposed classification formula and the public opinion thesaurus. It will classify one Tibetan text to one public opinion class. In this paper, the software is developed to automatically collect Tibetan web pages from the network, preprocess the web pages, extract the text features and hot words, discover the sensitive words and classify the Tibetan text to one public opinion class. The experiment shows that the Tibetan hot words extraction is effective and Tibetan classification results of public opinion are significant.

A New Kind of Parallel K_NN Network Public Opinion Classification Algorithm Based on Hadoop Platform

According to the network public opinion’s characteristics of distributed, massive and heterogeneous, a new kind of network public opinion classification method based on K_ nearest neighbor (K_NN) classification algorithm in Hadoop plateform is studied. The classification ability and execution efficiency of proposed scheme is verified and applied to the network public opinion documents classification test. The results show that the parallel K_NN algorithm can achieve rapid and accurate classification of network public opinion.

Study of a New Parallel K_NN Network Public Opinion Classification Algorithm Based on Hadoop Environment

A new kind of network public opinion classification method based on K_ nearest neighbor (K_NN) classification algorithm in Hadoop environment is studied in this paper. In the light of distributed storage and parallel processing Characteristics of Hadoop platform, the parallel K_NN classification algorithm in the frame of MapReduce is designed. The classification ability and execution efficiency of proposed scheme is verified and the results show that the parallel K_NN algorithm enhances the network public opinion classification precision and execution efficiently.

Study of Network Public Opinion Classification Method Based on Naive Bayesian Algorithm in Hadoop Environment

In the light of the excellent distributed storage and parallel processing feature of hadoop cluster, a new kind of network public opinion classification method based on Naive Bayes algorithm in hadoop environment is studied. The collected public opinion documents are stored locally according to the HDFS architecture, and whose character words are extracted paralleled in Mapreduce process. Thus the naive Bayesian classification algorithm is parallel encapsulated on cloud computing platform. The MapReduce packaged Naive Bayesian classification algorithm performance is verified and the results show that the algorithm execution speed are significantly improved compared to a single server. Its public opinion classification accuracy rate is more than 85%, which can effectively improve the classification performance of network public opinion and classification efficiency.

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.