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Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets
2017
Journal of Biomedical Semantics
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Method: We collected and manually annotated 6,000 HPV vaccines related tweets as a gold standard. SVM model was chosen and a hierarchical classification method was proposed and evaluated. ...
Results: A hierarchical classification scheme that contains 10 categories was built to access public opinions toward HPV vaccines comprehensively. ...
Availability of data and materials The annotations of gold corpus can be found at: https://sbmi.uth.edu/ ontology/files/TweetsAnnotationResults.zip. ...
doi:10.1186/s13326-017-0120-6
pmid:28253919
pmcid:PMC5335787
fatcat:525a3jvdwncldfqnc73baf4uoy
A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis
2020
IEEE Access
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In this study, we explore the possibility of using hierarchical clustering for twitter sentiment analysis. ...
A cooperative framework of SL, CL and AL is built to select the optimal cluster for tweets wherein the notion of optimal-cluster selection is operationalized using majority voting. ...
This revised version of twitter dataset, which consists of 2289 tweets, is used in this study. • STS-Test was collected for sentiment classification [7] . ...
doi:10.1109/access.2020.2983859
fatcat:yazthauwr5fzvifbwmq74jrsqu
ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets
2018
Proceedings of The 12th International Workshop on Semantic Evaluation
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the hierarchical attention network module for solving emotion and sentiment classification problem. ...
Our system consists of three main modules: preprocessing module, stacking module to solve the intensity prediction of emotion and sentiment, LSTM network module to solve multi-label classification, and ...
In order to better represent the semantics of emotion or sentiment, we utilize the hierarchical structure of a tweet to capture the contextual information of both intra and inter-tweet. ...
doi:10.18653/v1/s18-1042
dblp:conf/semeval/LiDFMCDLSL18
fatcat:h37iqervs5azjfbprru5c2umgy
Modeling Rich Contexts for Sentiment Classification with LSTM
[article]
2016
arXiv
pre-print
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While few prior study has approached the issue of modeling contexts in tweet, this paper proposes to use a hierarchical LSTM to model rich contexts in tweet, particularly long-range context. ...
Experimental results show that contexts can help us to perform sentiment classification remarkably better. ...
The long-range context indeed influences sentiment classification of a tweet. ...
arXiv:1605.01478v1
fatcat:pemlenj33rhqradhlzpp6zfcnm
Flat and Hierarchical Classifiers for Detecting Emotion in Tweets
[chapter]
2016
Lecture Notes in Computer Science
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This paper proposes a comparison between two approaches to emotion classification in tweets, taking into account six basic emotions. ...
Social media are more and more frequently used by people to express their feelings in the form of short messages. ...
Confusion matrix of the hierarchical classification based on TS2. ...
doi:10.1007/978-3-319-49130-1_5
fatcat:ryymgz2fmvcuzjwzfuy5xj34im
A Case Study on Social Media Analytics for Malaysia Budget
2021
International Journal of Advanced Computer Science and Applications
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However, the current scenario of tweets in Malaysia uses a combination of English-Malay words. Therefore, this study uses a hybrid of the corpusbased and support vector machine approach. ...
Overall, most netizens have a positive sentiment about Malaysia's Budget with 56.28% of the tweets being positive sentiments. ...
ACKNOWLEDGMENT The authors express their gratitude to the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia for supporting this study. ...
doi:10.14569/ijacsa.2021.0121064
fatcat:plpb3sewlfbijjiegcqanrbydq
Hierarchical Viewpoint Discovery from Tweets Using Bayesian Modelling
2018
Expert systems with applications
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Driven by the motivation that a viewpoint expressed in a tweet can be regarded as a path from the root to a leaf of a hierarchical viewpoint tree, the assignment of the relevant viewpoint topics is assumed ...
In this paper, we propose a novel Bayesian model for hierarchical viewpoint discovery from tweets. ...
This work was funded by the National Natural Science Foundation of China (61528302, 61772132), the Natural Science Foundation of Jiangsu Province of China (BK20161430) and Innovate UK (grant no. 103652 ...
doi:10.1016/j.eswa.2018.09.028
fatcat:mzgcmjxuxjb3rhzc22ztowirrm
Business Sentiment Quotient Analysis using Natural Language Processing
2020
International Journal of Engineering and Advanced Technology
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Number of tweets related to business are accessed from twitter and processed to estimate BSQ using python programming language. BSQ may be utilized for further Machine Learning Activities. ...
Analysis of the data/information will obviously produce useful inferences and many declarations. ...
Author found FastText as efficient in Hierarchical Text Classification. Author [10] has collected survey on different types of text feature extraction and selection for text classification. ...
doi:10.35940/ijeat.d8721.049420
fatcat:46lgdz4atjbsllmz3ywafpou6e
Classification of AI Powered Social Bots on Twitter by Sentiment Analysis and Data Mining through SVM
2019
International Journal of Computer Applications
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For detection, tweet syntax analysis, user behavior along with sentiment analysis is performed. Sentiment analysis is an opinion mining technique which analyzes people's opinions or sentiments. ...
Based on the resultant information the human or bot training and classification is made. After successfully training with SVM, this model was able to detect Twitter bots with a precision of 0.75. ...
Jacob [18] developed a module which uses the Hierarchical Classification for Sentiment Analysis. ...
doi:10.5120/ijca2019919701
fatcat:troqaah6y5gtzkknhupfi2n7uu
Opinion Mining on Twitter Data using Unsupervised Learning Technique
2016
International Journal of Computer Applications
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The results are also visualized using scatter plot graph and hierarchical graph. ...
Manual analysis of such large number of tweets is impossible. So the automated approach of unsupervised learning as spectral clustering is used. ...
Supervised learning have been popularly used and proven its effectiveness in sentiment classification. ...
doi:10.5120/ijca2016911317
fatcat:fbun4zzagvfxjp75jqfppgpqbe
Cross-domain informativeness classification for disaster situations
2018
Proceedings of the 10th International Conference on Management of Digital EcoSystems - MEDES '18
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Sentiment information is commonly used for text classification. To determine the sentiment of a tweet, the Python libraryTextblob 5 was used. ...
in tweet text is determining the sentiment of the tweet based on the language and terms used. ...
doi:10.1145/3281375.3281385
dblp:conf/medes/GrafRSPK18
fatcat:xk2s3pml6zentpefnwzjfmhmam
Mental Disorder Detection via Social Media Mining using Deep Learning
2020
Kinetik
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In the classification process, using LSTM Deep Learning obtained an accuracy of 70.89%; precision of 50.24%; recall 70.89%. ...
This study uses five criteria as a measure of mental health in a statement: sentiment, basic emotions, the use of personal pronouns, absolutist words, and negative words. ...
Also, this study conducted a deep learning classification of data that has been through the process of labelling using hierarchical clustering. Classification is done using several optimizers. ...
doi:10.22219/kinetik.v5i4.1120
fatcat:hl6nozv4pbd5revclxylqxmnza
Sentiment Lexicon Construction with Representation Learning Based on Hierarchical Sentiment Supervision
2017
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
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Experiments on the SemEval 2013-2016 datasets indicate that the sentiment lexicon generated by our approach achieves the state-of-the-art performance in both supervised and unsupervised sentiment classification ...
Sentiment lexicon is an important tool for identifying the sentiment polarity of words and texts. ...
Acknowledgements The work was supported by the Natural Science Foundation of China (No. 61672288), and the Natural Science Foundation of Jiangsu Province for Excellent Young Scholars (No. BK20160085). ...
doi:10.18653/v1/d17-1052
dblp:conf/emnlp/WangX17
fatcat:ya2ctpidk5cvxmnibym3tsyrnu
Sentiment Analysis of Arabic Reviews for Saudi Hotels Using Unsupervised Machine Learning
2020
Journal of Computer Science
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The main objective of this paper is to cluster Arabic reviews of Saudi hotels for sentiment analysis into positive and negative clusters. ...
These virtual worlds can be used for many aspects, because they are rich platforms full of feedback, emotions, thoughts and reviews. ...
Author's Contributions All authors have equally contributed to the final version of the manuscript.
Ethics This article is original and contains unpublished material. ...
doi:10.3844/jcssp.2020.1258.1267
fatcat:gposocngzzcclpuz72mleltv6a
Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques
2022
Computational Intelligence and Neuroscience
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The classification results showed that the features extracted from the tweets can be satisfactorily used to identify if a certain tweet is spam or not and create a learning model that will associate tweets ...
A lot of time and research has gone into effective ways to detect these forms of spam. Performing sentiment analysis on these posts can help us in solving this problem effectively. ...
70.28 70.16 66.55 Comparison of performance measures for different tweet sentiment classification models. ...
doi:10.1155/2022/5211949
pmid:35463239
pmcid:PMC9033328
fatcat:lkys2ojvfbdm7jlaccelykp5ue
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