Scalable multi-channel dilated CNN–BiLSTM model with attention mechanism for Chinese textual sentiment analysis

Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text. With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social media platforms has picked the interest of thousands of scientific researchers. Because the reviews, tweets and blogs acquired from these social media networks, act as a significant source for enhancing the decision making process. The obtained textual data (reviews, tweets, or blogs) are classified into three different class labels which are negative, neutral and positive for analyzing and extracting relevant information from the given dataset. In this contribution, we introduce an innovative MapReduce improved weighted ID3 decision tree classification approach for OM, which consists mainly of three aspects: Firstly We have used several feature extractors to efficiently detect and capture the relevant data from the given tweets, including N-grams or character-level, Bag-Of-Words, word embedding (GloVe, Word2Vec), FastText, and TF-IDF. Secondly, we have applied a multiple feature selector to reduce the high feature’s dimensionality, including Chi-square, Gain Ratio, Information Gain, and Gini Index. Finally, we have employed the obtained features to carry out the classification task using an improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3. In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature. Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature. This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS. Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle the massive datasets. We have carried out several experiences that aims to assess the effectiveness of our suggested classifier compared to some other contributions chosen from the literature. The experimental results demonstrated that our ID3 classifier works better on COVID-19_Sentiments dataset than other classifiers in terms of Recall (85.72 %), specificity (86.51 %), error rate (11.18 %), false-positive rate (13.49 %), execution time (15.95s), kappa statistic (87.69 %), F1-score (85.54 %), classification rate (88.82 %), false-negative rate (14.28 %), precision rate (86.67 %), convergence (it convergent towards the iteration 90), stability (it is more stable with mean deviation standard equal to 0.12 %), and complexity (it requires much lower time and space computational complexity).

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A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Along with the development of the Covid-19 pandemic, many responses and news were shared through social media. The new Covid-19 vaccination promoted by the government has raised pros and cons from the public. Public resistance to covid-19 vaccination will lead to a higher fatality rate. This study carried out sentiment analysis about the Covid-19 vaccine using the Support Vector Machine (SVM). This research aims to study the public response to the acceptance of the vaccination program. The research result can be used to determine the direction of government policy. Data collection was taken via Twitter in the year 2021. The data then undergoes the preprocessing methods. Afterward, the data is processed using SVM classification. Finally, the result is evaluated by a confusion matrix. The experimental result shows that SVM produces 56.80% positive, 33.75% neutral, and 9.45% negative. The highest model accuracy was obtained by RBF kernel of 92%, linear and polynomial kernels obtained 90% accuracy, and sigmoid kernel obtained 89% accuracy © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia

Sentiment analysis (SA) detects people’s opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer-based provide promising results for recognizing sentiment. Nonetheless, CNN has the advantage of extracting high-level features by using convolutional and max-pooling layers; it cannot efficiently learn a sequence of correlations. At the same time, Bidirectional RNN uses two RNN directions to improve extracting long-term dependencies. However, it cannot extract local features in parallel, and Transformer-based like Bidirectional Encoder Representations from Transformers (BERT) are the computational resources needed to fine-tune, facing an overfitting problem on small datasets. This paper proposes a novel attention-based model that utilizes CNNs with LSTM (named ACL-SA). First, it applies a preprocessor to enhance the data quality and employ term frequency-inverse document frequency (TF-IDF) feature weighting and pre-trained Glove word embedding approaches to extract meaningful information from textual data. In addition, it utilizes CNN’s max-pooling to extract contextual features and reduce feature dimensionality. Moreover, it uses an integrated bidirectional LSTM to capture long-term dependencies. Furthermore, it applies the attention mechanism at the CNN’s output layer to emphasize each word’s attention level. To avoid overfitting, the Guasiannoise and GuasianDroupout are adopted as regularization. The model’s robustness is evaluated on four English standard datasets, i.e., Sentiment140, US-airline, Sentiment140-MV, SA4A with various performance matrices, and compared efficiency with existing baseline models and approaches. The experiment results show that the proposed method significantly outperforms the state-of-the-art models.

Attention-Based CNN and Bi-LSTM Model Based on TF-IDF and GloVe Word Embedding for Sentiment Analysis

The SARS-CoV-2 coronavirus disease (COVID-19) pandemic continues to impact the health and well-being of the global population. The pandemic has shifted our view of the world to a different dimension. Control of the increasing spread of COVID-19 lies in vaccinating the public to halt the pandemic outbreak. However, there is uncertainty in the minds of the people regarding vaccines. In this research, we have analyzed the public tweets from Twitter related to COVID-19 vaccinations to detect the user’s view on the vaccination using Machine Learning (ML). Three different ML algorithms viz. Multinomial Na ive Bayes (MNB), Support Vector Machine (SVM), and Logistic Regression (LR) were used for the analysis of the Twitter data into positive or negative tweets. The LR algorithm gave the best results in the analysis with an accuracy of 97.3 %, whereas the accuracy of SVM was 96.26 %. The MNB had the lowest accuracy of 88 % in comparison to the other ML algorithms.

Sentiment Analysis of COVID-19 Vaccine Tweets Using Machine Learning

Microblogging websites like Twitter and Facebook, in this new era, is loaded with opinions and data. One of the most widely used micro-blogging site, Twitter, is where people share their ideas in the form of tweets and therefore it becomes one of the best sources for sentimental analysis. Opinions can be widely grouped into three categories good for positive, bad for negative and neutral and the process of analyzing differences of opinions and grouping them in all these categories is known as Sentiment Analysis. Data mining is basically used to uncover relevant information from web pages especially from the social networking sites. Merging data mining with other fields like text mining, NLP and computational intelligence we are able to classify tweets as good, bad or neutral. The main emphasis of this research is on the classification of emotions of tweets' data gathered from Twitter. In the past, researchers were using existing machine learning techniques for sentiment analysis but the results showed that existing machine learning techniques were not providing better results of sentiment classification. In order to improve classification results in the domain of sentiment analysis, we are using ensemble machine learning techniques for increasing the efficiency and reliability of proposed approach. For the same, we are merging Support Vector Machine with Decision Tree and experimental results prove that our proposed approach is providing better classification results in terms of f-measure and accuracy in contrast to individual classifiers.

Rachita Sharma

Papers

Sentiment Analysis of Tweets Using Machine Learning Approach