In this paper, sentiment analysis approaches, namely: lexicon-based and machine learning approaches, are applied and evaluated on an Arabic tweets dataset (short texts) regarding the Syrian civil war and crises. The experimental results revealed that machine learning approaches outperformed the lexicon-...
The under-specified and multilingual tweets are excluded from the Societal dataset for this analysis study. These tweets are considered to investigate whether the proposed model can address the challenge of social media noises. 5.1 How informative is a graph-based representation of a tweet compared ...
Our system plays a machine learning approach using sentiment analysis using tweet dataset. Nowadays people suffering from MI such as cardiac arrest, high blood pressure, congestive heart failure etc. Twitter is an excellent resource for the MI Patients since they connect people who have with similar...
55 Given the huge amount of dataset, informed consent was not possible to obtain in this study. However, anonimity is secured in this study, therefore the underlying data is not made published. Conclusions The public opinion and sentiment analysis on social media using an artificial intelligence ...
However, the Arabic language still lacks sufficient language resources to enable the tasks of sentiment analysis. In this paper, we present the details of collecting and constructing a large dataset of Arabic tweets. The techniques used in cleaning and pre-processing the collected dataset are ...
elimination of stop wordsetc are performed and then algorithms are applied on the remaining dataset. The correctness of those algorithms is checked using the results from the standard ALCHEMY API, and the accuracy of those algorithms is calculated individually. A new algorithm is proposed which is ...
Deep Learning on NLP in Pytorch using a Greek dataset with tweets regarding the elections . - Makri-Panagoula/Tweet-Sentiment-Analysis
PyFin-sentiment: Towards a machine-learning-based model for deriving sentiment from financial tweets Responding to the poor performance of generic automated sentiment analysis solutions on domain-specific texts, we collect a dataset of 10,000 tweets discus... M Wilksch,O Abramova - 《Int.j.inf....
As part of this work, we contributed a dataset consisting of approximately 10, 000 tweets, each labeled on a five point sentiment scale by three different people. Experiments demonstrate a detection rate between approximately 70% and an average false alarm rate of approximately 18% across all ...
In [7], the researchers introduced a model called ArSarcasm based on creating an Arabic sarcasm dataset. This model was produced by the re-labeling of the Arabic sentiment analysis datasets. It includes annotated data that has 10,547, where 16% of it is sarcastic. Not only the data was ...