English revision for a research paper
Budget: $10 – $30 USD
a research paper of a total of 17 pages out of which there are 2 pages references (not to be revised) a total of another 4-5 pages of figures (again not to be revised) and the remainder of paper is 11-12 pages that are required to be revised. The revision is required for grammatical, syntax, and other punctuation mistakes. There are few long sentences that must be divided into shorter ones.
About you:
1. you must be a native English speaker
2. you must have studied English or have a certificate as a professional proofreader.
3. preferable if you also have a computer science or engineering background.
Below is the paper abstract. Pls revise it and send it back as a proof of your skills and for me to be able to judge your abilities to correct the full paper.
Sentiment analysis has attracted the attention of Egyptian Decision-makers in the education sector as it offers a viable method to assess education quality services based on the students’ feedback as well as provides an understanding their needs. As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels, this research uses a dataset for tweets' sentiments to assess a few machine learning techniques. after dataset preprocessing to remove symbols and perform necessary Stemming and Lemmatization for features extraction. Followed by one of several machine learning techniques along with a proposed Long Short-Term Memory (LSTM) classifier optimized by the Salp Swarm Algorithm (SSA) and measuring the corresponding performance. Then, the validity and accuracy of commonly used classifiers, such as Support Vector Machine, Logistic Regression Classifier, and Naive Bayes classifier were reviewed. Moreover, LSTM based on the SSA classification model was compared with Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB). Finally, as LSTM based SSA achieved the highest accuracy, it was applied to predict the sentiments of students’ feedback and evaluate their association with the course outcome evaluations for education quality purposes.
About you:
1. you must be a native English speaker
2. you must have studied English or have a certificate as a professional proofreader.
3. preferable if you also have a computer science or engineering background.
Below is the paper abstract. Pls revise it and send it back as a proof of your skills and for me to be able to judge your abilities to correct the full paper.
Sentiment analysis has attracted the attention of Egyptian Decision-makers in the education sector as it offers a viable method to assess education quality services based on the students’ feedback as well as provides an understanding their needs. As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels, this research uses a dataset for tweets' sentiments to assess a few machine learning techniques. after dataset preprocessing to remove symbols and perform necessary Stemming and Lemmatization for features extraction. Followed by one of several machine learning techniques along with a proposed Long Short-Term Memory (LSTM) classifier optimized by the Salp Swarm Algorithm (SSA) and measuring the corresponding performance. Then, the validity and accuracy of commonly used classifiers, such as Support Vector Machine, Logistic Regression Classifier, and Naive Bayes classifier were reviewed. Moreover, LSTM based on the SSA classification model was compared with Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB). Finally, as LSTM based SSA achieved the highest accuracy, it was applied to predict the sentiments of students’ feedback and evaluate their association with the course outcome evaluations for education quality purposes.
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