A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis

Abstract

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.

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

“…The lexicon method integrates sentiment lexicons which are comprised of a set of rules for the classification of words from the text as positive, negative, or neutral [35]. The foundation of lexicon-based sentiment classification is that the polarity of a text can be decided using the polarity of sentimentbearing words in a given text.…”

“…The study suggested that 76% of the reviews were following the numeric rating whereas, 24% of the reviews and their corresponding numeric rating were not in correlation with each other. Along the same lines, [35] suggested that using TextBlob for sentiment analysis enhances the performance of models. In the light of the above discussion, we performed the self-annotation of the unannotated dataset using sentiment lexicons.…”

“…It provides simple APIs for several NLP tasks such as sentiment analysis, noun phrase extraction, parts-ofspeech tagging, translation, and classification, etc. The authors in [35] use TextBlob as a sentiment analyzer to predict the polarity of the tweets. For sentiment analysis, TextBlob has a pattern processing and natural language toolkit (NLTK) [38].…”

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

“…Numerous research groups have exploited the state-of-art of machine learning to catalog and analyze the large flows of COVID-19-related information circulating on social networks, forums, and online platforms like Twitter, Reddit, Instagram, Facebook, and YouTube ( Tsao et al, 2021 ). Among the most skillful approaches, Rustam et al adopted a wide variety of supervised algorithms such as random forest (RF), XGBoost classifier, support vector classifier (SVC), extra trees classifier (ETC), decision tree (DT), and long-short term memory (LSTM) deep learning model to analyze COVID-19-related tweets sentiment ( Rustam et al, 2021 ). Their results showed that: 1) Extra Trees Classifiers outperformed all other models by achieving a 0.93 accuracy score using the authors’ proposed concatenated features set; 2) the LSTM achieved low accuracy as compared to machine learning classifiers.…”

Secured cross‐layer cross‐domain routing in dense wireless sensor network: A new hybrid based clustering approach

“…WSNs are deployed in diverse fields of appliances due to their low‐cost and easier deployment characteristics 5–7 . Its appliances are familiar in areas such as home security, railway monitoring, military supervision, smart buildings, and agricultural field, health care, and remote monitoring 8–11 . Nevertheless, there are certain challenges, which should be prevailed while designing the WSN for the next generation.…”