Incremental learning with partial-supervision based on hierarchical Dirichlet process and the application for document classification

Wang, Di; Alrubaie, Ahmad

doi:10.1016/j.asoc.2015.04.044

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…5 Compared with other machine learning methods, no keywords are needed to be pre-defined for LDA itself which makes it more attractive for text mining. We proved the higher accuracy of our proposed improved LDA over SVM and other algorithms in our previous work (Wang et al 2017;Wang and Al-Rubaie 2015).…”

Section: Improved Lda Classifier For Sentiment Analysis For Benchmark Datasetssupporting

confidence: 58%

“…Tweet classification accuracy is key. We applied our improved version of LDA for tweet classification which has achieved better accuracy for general classification problems for tweets (Wang et al 2017;Wang and Al-Rubaie 2015 ). 2 The pseudocode of the filtering and sentiment analysis process is shown in Fig.…”

Section: Lda Sentiment Classifiermentioning

confidence: 99%

“…6. The detailed algorithm for our improved LDA is included in the references (Wang et al 2017; Wang and Al-Rubaie 2015) 3 and pseudocode is included in reference (Wang and Al-Rubaie 2015). A patent application has been filed on the improved LDA method, and more details are in reference 4 .…”

Section: Lda Sentiment Classifiermentioning

confidence: 99%

“…Then, the proposed improved LDA is applied to tweets that are not pre-filtered as junk/sentimentirrelevant. The proposed improved LDA is a supervised learning method which needs a training file to generate the model (Wang et al 2017;Wang and Al-Rubaie 2015). 6 The training file contains a list of tweets (after filtering) that are manually tagged as positive, negative, neutral or junk/irrelevant, because the remaining tweets still contain some junk tweets that could not be filtered out in Stages 1 and 2 filtering.…”

Section: Accuracy For General Sentiment Analysismentioning

confidence: 99%

“…Achieve a better overall accuracy for general sentiment analysis. We applied our proposed improved LDA for tweets (Wang et al 2017;Wang and Al-Rubaie 2015) 1 classification to achieve similar or better accuracy for sentiment analysis than human being tagging. The proposed method is proved to obtain better results on benchmark datasets than the state-of-the-art methods.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

National happiness index monitoring using Twitter for bilanguages

Wang

Alrubaie

Hirsch

et al. 2021

Soc. Netw. Anal. Min.

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Nowadays, social media have become one of the most important methods of communication that provide a real-time and rich source of information, including sentiments. Understanding the population sentiment is a key goal for organisations and governments. In recent years, quite a lot of research has been done on sentiment analysis from social media. However, all the work in the state of the art is focused on a specific pre-defined subset of tweets, e.g. sentiment analysis via keywords search from tweets for relevant brands, products, services, events and so forth. Monitoring the general sentiment at national level through the whole social media stream is not done due to the challenges of filtering sentiment-irrelevant information, diversity of vocabulary usage in general tweets across topics causing low accuracy and the need for bilingual or multilingual models. This paper proposes a system for general population sentiment monitoring from a social media stream (Twitter), through comprehensive multi-level filters, and our proposed improved latent Dirichlet allocation (LDA) (Wang et al. in ACM Trans Internet Technol 18(1):1–23, 2017; Wang and Al-Rubaie in Appl Soft Comput 33:250–262, 2015; https://patents.google.com/patent/US20170293597A1/en) method for sentiment classification. Experiments show that our proposed improved LDA for sentiment analysis yields the best results, and also validate our proposed system for national sentiment monitoring in Abu Dhabi using twitter.

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Section: Improved Lda Classifier For Sentiment Analysis For Benchmark Datasetssupporting

confidence: 58%

Section: Lda Sentiment Classifiermentioning

confidence: 99%