scholarly journals Exploration of Twitter Sentiments and Classification by using Deep CNN and Naive Bayes

2020 ◽  
Vol 9 (2) ◽  
pp. 1100-1105
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Unsupervised Learning ◽  
Naive Bayes ◽  
Preceding Paper ◽  
Naïve Bayes ◽  
Statistical Characteristics ◽  
Learning Approach ◽  
Semantic Relationships ◽  
Deep Cnn

Sentiment evaluation of tweets help the enterprises to evaluate public emotion towards the activities or products associated with them. Most of the research targeted to obtain sentiment capabilities with the help of analyzing syntactic and lexical features which can be expressed through sentiment phrases, emoticons, exclamation marks etc. In the proposed paper we introduce a phrase embedding received by means of unsupervised learning(deep learning) on large twitter texts which uses contextual semantic relationships and co-occurrence statistical characteristics between words in tweets and also consider the emojis to categorise the emotions whether it is positive or negative by the use of Naive Bayes. In the preceding paper which used usnsupervised learning approach for classification, has an accuracy of 87% and supervised has an accuracy of 89%. According to our context, Naive Bayes has given an accuracy of 100% and CNN has given an accuracy of 100%. As compared to machine learning. It has a higher performance on the accuracy, precision and recall.

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data

2015 ◽  
Vol 34 (21) ◽  
pp. 2941-2957 ◽  
Author(s):  
Julian Wolfson ◽  
Sunayan Bandyopadhyay ◽  
Mohamed Elidrisi ◽  
Gabriela Vazquez-Benitez ◽  
David M. Vock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning Approach ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Machine Learning Approach

scholarly journals Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

2021 ◽  
Vol 1 (1) ◽  
pp. 57-70
Author(s):  
Ranjan Raj Aryal ◽  
Ankit Bhattarai
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Other ◽  
Learning Approach ◽  
Asian Countries ◽  
Vaccination Programs ◽  
Machine Learning Approach ◽  
Positive Sentiment

Social media is one platform where people share their opinions and views on different topics, services, or behaviors that happen around them. Since the COVID19 pandemic that started at the end of 2019, it has been a topic on which people express their sentiments. Recently, the COVID19 vaccination programs have got a lot of responses. In this paper, we have proposed two models: one based on the machine learning approach: Naive Bayes & the other based on deep learning: LSTM, whose goal is to know the sentiment of Asian region tweets towards the vaccine through sentiment analysis. The data were extracted with the help of Twitter API from March 23, 2021, till April 2, 2021. The extraction approach contains keywords with geocoding of some of the Asian countries, especially Nepal, India and Singapore. After collecting data, some preprocessing such as removing numbers, non-English & stop words, removing special characters, and hyperlinks were done. The polarity of tweets was assigned using the Text blob library. The tweets were classified into one of the three: positive, negative, or neutral. Now the data were preprocessed with the splitting of tweets into training & testing sets. Both the models were trained & tested using 10767 unique tweets. This experiment shows that a number of people in these three countries (Nepal, India and Singapore) have positive sentiment towards the vaccine and are taking the first dose of Covid19 vaccine. At last, the accuracy of the LSTM model was found to be 7% greater than that of the Naive Bayes-based model.

Comparing Machine Learning and Deep Learning Methods for Real-Time Crash Prediction

2019 ◽  
Vol 2673 (8) ◽  
pp. 169-178 ◽  
Author(s):  
Athanasios Theofilatos ◽  
Cong Chen ◽  
Constantinos Antoniou
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Data Set ◽  
Real Time Traffic ◽  
The Impact

Although there are numerous studies examining the impact of real-time traffic and weather parameters on crash occurrence on freeways, to the best of the authors’ knowledge there are no studies which have compared the prediction performances of machine learning (ML) and deep learning (DL) models. The present study adds to current knowledge by comparing and validating ML and DL methods to predict real-time crash occurrence. To achieve this, real-time traffic and weather data from Attica Tollway in Greece were linked with historical crash data. The total data set was split into training/estimation (75%) and validation (25%) subsets, which were then standardized. First, the ML and DL prediction models were trained/estimated using the training data set. Afterwards, the models were compared on the basis of their performance metrics (accuracy, sensitivity, specificity, and area under curve, or AUC) on the test set. The models considered were k-nearest neighbor, Naïve Bayes, decision tree, random forest, support vector machine, shallow neural network, and, lastly, deep neural network. Overall, the DL model seems to be more appropriate, because it outperformed all other candidate models. More specifically, the DL model managed to achieve a balanced performance among all metrics compared with other models (total accuracy = 68.95%, sensitivity = 0.521, specificity = 0.77, AUC = 0.641). It is surprising though that the Naïve Bayes model achieved a good performance despite being far less complex than other models. The study findings are particularly useful, because they provide a first insight into performance of ML and DL models.

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