Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams

Abstract

The exponential growth of social media users has changed the dynamics of retrieving the potential information from user-generated content and transformed the paradigm of information-retrieval mechanism with the novel developments on the concept of “web of data”. In this regard, our proposed Ontology-Based Sentiment Analysis provides two novel approaches: First, the emotion extraction on tweets related to COVID-19 is carried out by a well-formed taxonomy that comprises possible emotional concepts with fine-grained properties and polarized values. Second, the potential entities present in the tweet can be analyzed for semantic associativity. The extraction of emotions can be performed in two cases: (i) words directly associated with the emotional concepts present in the taxonomy and (ii) words indirectly present in the emotional concepts. Though the latter case is very challenging in processing the tweets to find the hidden patterns and extract the meaningful facts associated with it, our proposed work is able to extract and detect almost 81% of true positives and considerably able to detect the false negatives. Finally, the proposed approach's superior performance is witnessed from its comparison with other peer-level approaches.

Ensemble Text Summarization Model for COVID-19-Associated Datasets

“…Te initial data processing and data cleaning tasks were applied to the dataset to fne-tune the dataset suitable to build the model more efectively and efciently. We used the Keras library [51][52][53] to remove the stop words, drop the duplication, and avoid the NA (not available) summary/text values. Te unwanted symbol characters and punctuations were removed potentially without afecting the objective of the solution.…”

Effective Preprocessing and Normalization Techniques for COVID-19 Twitter Streams with POS Tagging via Lightweight Hidden Markov Model

“…In addition to finding the OOV words present in the tweets, there are also some other factors to be considered for effective normalization such as stemming, lemmatization, stop word removal, and emoticons detections [37][38][39]. Extra supervision is required to handle these preprocessing methods and further, these methods help to provide contextual support for sentiment analysis, word cluster, information extraction, entity detection, and many more (see Table 6).…”