A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier
Creators: name: Qadir, Junaid
Publisher: name: figshare
Year: 2021
Subjects: Computer Engineering

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