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Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets

Jingcheng Du, Jun Xu, Hsingyi Song, Xiangyu Liu, Cui Tao
2017 Journal of Biomedical Semantics  
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: ontology/files/  ... 
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

Maryum Bibi, Wajid Aziz, Majid Almarashi, Imtiaz Hussain Khan, Malik Sajjad Ahmed Nadeem, Nazneen Habib
2020 IEEE Access  
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

Meng Li, Zhenyuan Dong, Zhihao Fan, Kongming Meng, Jinghua Cao, Guanqi Ding, Yuhan Liu, Jiawei Shan, Binyang Li
2018 Proceedings of The 12th International Workshop on Semantic Evaluation  
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]

Minlie Huang, Yujie Cao, Chao Dong
2016 arXiv   pre-print
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]

Giulio Angiani, Stefano Cagnoni, Natalia Chuzhikova, Paolo Fornacciari, Monica Mordonini, Michele Tomaiuolo
2016 Lecture Notes in Computer Science  
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

Ahmad Taufiq Mohamad, Nur Atiqah Sia Abdullah
2021 International Journal of Advanced Computer Science and Applications  
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

Lixing Zhu, Yulan He, Deyu Zhou
2018 Expert systems with applications  
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  
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

Abu Foysal, Safat Islam, Touhidur Rahaman
2019 International Journal of Computer Applications  
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

Muqtar Unnisa, Ayesha Ameen, Syed Raziuddin
2016 International Journal of Computer Applications  
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

David Graf, Werner Retschitzegger, Wieland Schwinger, Birgit Pröll, Elisabeth Kapsammer
2018 Proceedings of the 10th International Conference on Management of Digital EcoSystems - MEDES '18  
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

Binti Kholifah, Iwan Syarif, Tessy Badriyah
2020 Kinetik  
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

Leyi Wang, Rui Xia
2017 Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing  
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

Samar Alosaimi, Maram Alharthi, Khloud Alghamdi, Tahani Alsubait, Tahani Alqurashi
2020 Journal of Computer Science  
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

Anisha P Rodrigues, Roshan Fernandes, Aakash A, Abhishek B, Adarsh Shetty, Atul K, Kuruva Lakshmanna, R. Mahammad Shafi, Muhammad Ahmad
2022 Computational Intelligence and Neuroscience  
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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