Performance Comparison of Different Lexicons for Sentiment Analysis in Arabic

2016 ◽  
Author(s):  
Hunaida Awwad ◽  
Adil Alpkocak
Keyword(s):  
Sentiment Analysis ◽  
Performance Comparison

scholarly journals A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245909
Author(s):  
Furqan Rustam ◽  
Madiha Khalid ◽  
Waqar Aslam ◽  
Vaibhav Rupapara ◽  
Arif Mehmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Performance Comparison ◽  
Supervised Machine Learning ◽  
Accuracy Score ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Analysis Technique ◽  
Realistic Assessment

The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.

Performance Comparison of Deep Learning Algorithms for Sentiment Analysis

2021 ◽  
Author(s):  
Aryaman Sriram ◽  
Diptanshu Sinha ◽  
Valarmathi B ◽  
Srinivasa Gupta N
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Performance Comparison

scholarly journals Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter

2021 ◽  
Vol 13 (2) ◽  
pp. 168-174
Author(s):  
Rifqatul Mukarramah ◽  
Dedy Atmajaya ◽  
Lutfi Budi Ilmawan
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Confusion Matrix ◽  
Classification Performance ◽  
Performance Comparison ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Testing Method

Sentiment analysis is a technique to extract information of one’s perception, called sentiment, on an issue or event. This study employs sentiment analysis to classify society’s response on covid-19 virus posted at twitter into 4 polars, namely happy, sad, angry, and scared. Classification technique used is support vector machine (SVM) method which compares the classification performance figure of 2 linear kernel functions, linear and polynomial. There were 400 tweet data used where each sentiment class consists of 100 data. Using the testing method of k-fold cross validation, the result shows the accuracy value of linear kernel function is 0.28 for unigram feature and 0.36 for trigram feature. These figures are lower compared to accuracy value of kernel polynomial with 0.34 and 0.48 for unigram and trigram feature respectively. On the other hand, testing method of confusion matrix suggests the highest performance is obtained by using kernel polynomial with accuracy value of 0.51, precision of 0.43, recall of 0.45, and f-measure of 0.51.

Direct ammonia fueled solid oxide fuel cells: A comprehensive review on challenges, opportunities and future outlooks

2020 ◽  
pp. 70-91 ◽  
Author(s):  
Molla Asmare ◽  
Mustafa Ilbas
Keyword(s):  
Electric Power ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Clean Energy ◽  
Performance Comparison ◽  
Optimum Operating ◽  
Practical Implementation ◽  
Human Society ◽  
Energy Access ◽  
Green Fuel

Nowadays, the most decisive challenges we are fronting are perfectly clean energy making for equitable and sustainable modern energy access, and battling the emerging alteration of the climate. This is because, carbon-rich fuels are the fundamental supply of utilized energy for strengthening human society, and it will be sustained in the near future. In connection with this, electrochemical technologies are an emerging and domineering tool for efficiently transforming the existing scarce fossil fuels and renewable energy sources into electric power with a trivial environmental impact. Compared with conventional power generation technologies, SOFC that operate at high temperature is emerging as a frontrunner to convert the fuels chemical energy into electric power and permits the deployment of varieties of fuels with negligible ecological destructions. According to this critical review, direct ammonia is obtained as a primary possible choice and price-effective green fuel for T-SOFCs. This is because T-SOFCs have higher volumetric power density, mechanically stable, and high thermal shocking resistance. Also, there is no sealing issue problem which is the chronic issues of the planar one. As a result, the toxicity of ammonia to use as a fuel is minimized if there may be a leakage during operation. It is portable and manageable that can be work everywhere when there is energy demand. Besides, manufacturing, onboard hydrogen deposition, and transportation infrastructure connected snags of hydrogen will be solved using ammonia. Ammonia is a low-priced carbon-neutral source of energy and has more stored volumetric energy compared with hydrogen. Yet, to utilize direct NH3 as a means of hydrogen carrier and an alternative green fuel in T-SOFCs practically determining the optimum operating temperatures, reactant flow rates, electrode porosities, pressure, the position of the anode, thickness and diameters of the tube are still requiring further improvement. Therefore, mathematical modeling ought to be developed to determine these parameters before planning for experimental work. Also, a performance comparison of AS, ES, and CS- T-SOFC powered with direct NH3 will be investigated and best-performed support will be carefully chosen for practical implementation and an experimental study will be conducted for verification based on optimum parameter values obtained from numerical modeling.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

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