Text Classification on Twitter Data

Harjule, Priyanka; Gurjar, Astha; Seth, Harshita; Thakur, Priya

doi:10.1109/icetce48199.2020.9091774

Cited by 21 publications

(15 citation statements)

References 12 publications

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“…Harjule et al implemented multinomial Naive Bayes, logistic regression, support vector machine and RNN on already categorized data sets to perform sentiment analysis. In this paper, seperate topic modelling algorithm is applied to automatically categorize the data set, and further classification algorithms are implemented [8].…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Abstract-Based Classification of Medical Journals Using Machine Learning Techniques

Deepika¹,

Radha²

2021

Computer Networks and Inventive Communication Technologies

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Researchers face many challenges in finding the opt web-based resources by giving the queries based on keyword search. Due to advent of Internet, there are huge biological literatures that are deposited in the medical database repository in recent years. Nowadays, as many web-based medical researchers evolved in the field of medicine, there is need for an intelligent and efficient extraction technique required to filter appropriate and opt literature from the growing body of biomedical literature repository. In this research work, new combination of model is proposed in order to find the new insights in applying the combination of algorithm on biological data set. The information in the biomedical field is the basic information for healthy living. National Center for Biotechnology Information (NCBI)'s PubMed is the major source of peer-reviewed biomedical documents for researchers and health practitioners in the field of health-related management. In this paper, abstracts available in PubMed database is used for experimentation. In recent years, deep learning-based neural approach models provide an efficient way to create an end-to-end model that can accurately measure classification labels. This research work is a systematic analysis of performance of the supervised learning models such as Naïve Bayes (NB), support vector machine (SVM) and long short-term memory (LSTM) by implementing on textual medical data. The novelty in this work is the process of incorporating certain topic modelling techniques after the pre-processing phase to automatically label the documents. Topic modelling is a useful technique in increasing the efficiency and improves the ability of researchers to interpret biological information. So, the classification algorithms thus proposed are implemented in combination with popular topic modelling algorithms such as latent Dirichlet algorithm (LDA) and non-negative matrix factorization (NMF). The final performance of the combination of algorithms is also analysed and is found that SVM with NMF outperforms the other models.

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Section: Related Workmentioning

confidence: 99%

Performance Analysis of Abstract-Based Classification of Medical Journals Using Machine Learning Techniques

Deepika¹,

Radha²

2021

Computer Networks and Inventive Communication Technologies

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“…The authors studied the use of two machine learning (ML) algorithms, namely random forest (RF), and support vector machine (SVM) and found that SVM performed best in classifying the accident narratives. Multinomial Naive Bayes (MNB), Logistic Regression (LR), Support Vector Machines were also deployed in [21] to prediction task on Twitter Data.…”

Section: Related Work On Text Classificationmentioning

confidence: 99%

Deep Learning Approach for Automatic Topic Classification in an Online Submission System

Điện¹,

Thanh-Hai²,

Nguyen³

2020

Adv. sci. technol. eng. syst. j.

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Topic classification is a crucial task where knowledge categories exist within hierarchical information systems designed to facilitate knowledge search and discovery. An application of topic classification is article (e.g., journal/conference paper) classification which is very useful for online submission systems. In fact, numerous online journals/magazine submission systems usually receive thousands of article submissions or even more for each month. This leads to a huge amount of time-consumption of editors to process and categorize the submissions aiming to look for and assign appropriate reviewers to the submitted articles. In this study, we propose an approach based on natural language processing techniques and machine learning algorithms (both classic machine learning and deep learning) to automatic classify the topics of articles in an online submission system. We show by promising performance collected from prediction tasks to present that the proposed method is a potential approach for applying to the real system.

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“…However, the SVM were not suitable for the five classifications in this study because it is basically suitable for two classes of classification. [16][17][18] The results of the overall evaluation and the labeled results are shown in Table 5. The % result is the value for each rating and item divided by the overall number 2,392.…”

Section: Machine Learning Accuracymentioning

confidence: 99%

A study of the possibilities of text mining and machine learning for score evaluation and review content

Taniguchi¹,

Tsuda²

2020

AIR

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With the widespread use of the Internet, there are more and more opportunities to purchase a variety of products through online shopping. The opportunities are not only for small products such as books, but also for home appliances. Previously, when purchasing a product, users who wanted to buy a product would visit a store and get expert advice on what to buy. Now, however, customers consider reviews on the Internet to be more important information for considering the products to be purchased. And evaluation page consists of an overall evaluation, an evaluation of each feature, and comments, which are word of mouth. The overall evaluation and the evaluation of each feature is often a score evaluation, and organized information such as the average and the distribution of scores are presented. However, it is difficult to read all the comments that are word of mouth because they are often enumerated as is. Therefore, in this study, we created a system to label which features people commented on in response to the word of mouth comments using data from the TV’s comprehensive evaluation page. 2392 TV evaluation results from Sony.com were used. From the extracted data, text mining was performed on the comments, which are word of mouth, followed by labels of which features are commented on. When 80\% of the test data was prepared and implemented against 20\% of the learning data, the label was predicted with 77\% accuracy. From this study, we used text mining to label the comments, which are customer impression. from the current study, text mining was used to label the comments, which are customer impression. The results and score ratings were used to identify customer trends.

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Text Classification on Twitter Data

Cited by 21 publications

References 12 publications

Performance Analysis of Abstract-Based Classification of Medical Journals Using Machine Learning Techniques

Performance Analysis of Abstract-Based Classification of Medical Journals Using Machine Learning Techniques

Deep Learning Approach for Automatic Topic Classification in an Online Submission System

A study of the possibilities of text mining and machine learning for score evaluation and review content

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