scholarly journals Optimization of sentiment analysis using machine learning classifiers

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Jaspreet Singh ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Myers-briggs Personality Prediction and Sentiment Analysis of Twitter using Machine Learning Classifiers and BERT

2021 ◽  
Vol 13 (6) ◽  
pp. 48-60
Author(s):  
Prajwal Kaushal ◽  
◽  
Nithin Bharadwaj B P ◽  
Pranav M S ◽  
Koushik S ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Analysis Model ◽  
Research Recruitment ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Myers Briggs ◽  
Personality Prediction ◽  
Twitter Users ◽  
Psychological Instruments

Twitter being one of the most sophisticated social networking platforms whose users base is growing exponentially, terabytes of data is being generated every day. Technology Giants invest billions of dollars in drawing insights from these tweets. The huge amount of data is still going underutilized. The main of this paper is to solve two tasks. Firstly, to build a sentiment analysis model using BERT (Bidirectional Encoder Representations from Transformers) which analyses the tweets and predicts the sentiments of the users. Secondly to build a personality prediction model using various machine learning classifiers under the umbrella of Myers-Briggs Personality Type Indicator. MBTI is one of the most widely used psychological instruments in the world. Using this we intend to predict the traits and qualities of people based on their posts and interactions in Twitter. The model succeeds to predict the personality traits and qualities on twitter users. We intend to use the analyzed results in various applications like market research, recruitment, psychological tests, consulting, etc, in future.

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.

scholarly journals Tweets Classification on the Base of Sentiments for US Airline Companies

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1078 ◽  
Author(s):  
Furqan Rustam ◽  
Imran Ashraf ◽  
Arif Mehmood ◽  
Saleem Ullah ◽  
Gyu Choi
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Sentiment Analysis ◽  
Stochastic Gradient Descent ◽  
Ensemble Classifiers ◽  
Term Frequency ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Lower Accuracy ◽  
The Impact

The use of data from social networks such as Twitter has been increased during the last few years to improve political campaigns, quality of products and services, sentiment analysis, etc. Tweets classification based on user sentiments is a collaborative and important task for many organizations. This paper proposes a voting classifier (VC) to help sentiment analysis for such organizations. The VC is based on logistic regression (LR) and stochastic gradient descent classifier (SGDC) and uses a soft voting mechanism to make the final prediction. Tweets were classified into positive, negative and neutral classes based on the sentiments they contain. In addition, a variety of machine learning classifiers were evaluated using accuracy, precision, recall and F1 score as the performance metrics. The impact of feature extraction techniques, including term frequency (TF), term frequency-inverse document frequency (TF-IDF), and word2vec, on classification accuracy was investigated as well. Moreover, the performance of a deep long short-term memory (LSTM) network was analyzed on the selected dataset. The results show that the proposed VC performs better than that of other classifiers. The VC is able to achieve an accuracy of 0.789, and 0.791 with TF and TF-IDF feature extraction, respectively. The results demonstrate that ensemble classifiers achieve higher accuracy than non-ensemble classifiers. Experiments further proved that the performance of machine learning classifiers is better when TF-IDF is used as the feature extraction method. Word2vec feature extraction performs worse than TF and TF-IDF feature extraction. The LSTM achieves a lower accuracy than machine learning classifiers.

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