Deep Learning‐Based Methods for Sentiment Analysis on Nepali COVID‐19‐Related Tweets

Abstract

COVID-19 has claimed several human lives to this date. People are dying not only because of physical infection of the virus but also because of mental illness, which is linked to people’s sentiments and psychologies. People’s written texts/posts scattered on the web could help understand their psychology and the state they are in during this pandemic. In this paper, we analyze people’s sentiment based on the classification of tweets collected from the social media platform, Twitter, in Nepal. For this, we, first, propose to use three different feature extraction methods—fastText-based (ft), domain-specific (ds), and domain-agnostic (da)—for the representation of tweets. Among these three methods, two methods (“ds” and “da”) are the novel methods used in this study. Second, we propose three different convolution neural networks (CNNs) to implement the proposed features. Last, we ensemble such three CNNs models using ensemble CNN, which works in an end-to-end manner, to achieve the end results. For the evaluation of the proposed feature extraction methods and CNN models, we prepare a Nepali Twitter sentiment dataset, called NepCOV19Tweets, with 3 classes (positive, neutral, and negative). The experimental results on such dataset show that our proposed feature extraction methods possess the discriminating characteristics for the sentiment classification. Moreover, the proposed CNN models impart robust and stable performance on the proposed features. Also, our dataset can be used as a benchmark to study the COVID-19-related sentiment analysis in the Nepali language.

A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification

“…Several works on fundamental NLP applications such as part of speech tagging [ 17 ], named entity recognition [ 18 ], and text classification [ 14 , 19 ] have been reported in the literature. However, there exists only one work by Sitaula et al [ 8 ] for COVID-19-related tweets sentiment analysis in the Nepali language although there are several recent works conducted in other languages, such as English. Therefore, we review the overall research works carried out in sentiment classification based on COVID-19-related tweets in different languages, including both Nepali and non-Nepali languages.…”

“…Although most of the works in COVID-19-related sentiment analysis are conducted in the English language, there are few works reported in other languages too such as Arabic [ 7 ], Brazilian [ 23 ], and Nepali [ 8 ] using both traditional machine learning and deep learning approaches. Specifically, the authors in [ 7 ] represented the COVID-19 tweets in the Arabic language using unigram and bigram coupled with TF-IDF approach and classified using various machine learning algorithms such as SVM, K -Nearest Neighbour (KNN), and NB.…”

“…Furthermore, De et al [ 23 ] conducted the sentiment analysis on both Brazilian news articles and COVID-19-related tweets, which suggest both news and tweets provide similar kinds of sentiments. Recently, Sitaula et al [ 8 ] proposed the sentiment analysis of Nepali COVID-19 tweets using deep learning-based methods. They also published a benchmark dataset of COVID-19 tweets in the Nepali language, called, NepCov19Tweets dataset.…”

“…The widely used word embedding vectors for English languages are word2vec [ 9 ], GloVe [ 10 ], and FastText [ 11 ]. Herein, we choose FastText-based word embedding in our work because it is an open-source deep learning model pretrained on large Wikipedia corpus on Nepali language and a recent study on NepCov19Tweets dataset shows that FastText-based feature extraction method is promising for the classification of Nepali COVID-19-related tweets [ 8 ]. It produces the vector of size 300-D for each word/token.…”

“…For the syntactic information, we adopt the TF-IDF method, which analyses the keywords/tokens based on their occurrence patterns in the training documents. For the semantic information, we employ the FastText method as suggested by Sitaula et al [ 8 ], who carried out recent work in Nepali COVID-19-related tweets sentiment classification. Second, we compare nine different ML algorithms for the COVID-19 tweets sentiment classification.…”