Survey of Sentiment Analysis Using Deep Learning Techniques

M, Indhraom Prabha; Srikanth, G. Umarani

doi:10.1109/iciict1.2019.8741438

Cited by 47 publications

(18 citation statements)

References 20 publications

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“…Lots of work is also done in the sentiment analysis domain using the deep learning approach in history [20,21], as a study [22] proposed an approach for sentiments classification using a deep learning model. It uses NLP for topic modeling to find the core issues related to Covid-19 as expressed on social media.…”

Section: Plos Onementioning

confidence: 99%

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

et al. 2021

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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.

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Section: Plos Onementioning

confidence: 99%

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

et al. 2021

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“…Mukherjee et al (29) used the RNN model (bidirectional long short-term memory) to use NLP methods and utilize a bidirectional RNN to learn patterns of relations from textual data for sentiment analysis. There is much work done in tweet sentiment analysis using different DL and NLP approaches as mentioned in the study (30,31). This study proposed a sentiment classification approach using DL and NLP models (12).…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

Jalil

Abbasi

Javed

et al. 2022

Front. Public Health

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The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

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“…-It outperforms Word2vec in the tasks of terms analogies because it is based on leveraging global word to word co-occurrence counts leveraging the entire corpus [38].…”

Section: Glovementioning

confidence: 99%

“…-It assigns a smaller weight to very frequent term pairs in order to avoid meaningless terms such as "the", "a" [38].…”

Section: Glovementioning

confidence: 99%

Evaluation of different extractors of features at the level of sentiment analysis

Es-sabery¹,

Es-sabery²,

Garmani³

et al. 2022

Infocommunications journal

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Sentiment analysis is the process of recognizing and categorizing the emotions being expressed in a textual source. Tweets are commonly used to generate a large amount of sentiment data after they are analyzed. These feelings data help to learn about people's thoughts on a various range of topics. People are typically attracted for researching positive and negative reviews, which contain dislikes and likes, shared by the consumers concerning the features of a certain service or product. Therefore, the aspects or features of the product/ service play an important role in opinion mining. Furthermore to enough work being carried out in text mining, feature extraction in opinion mining is presently becoming a hot research field. In this paper, we focus on the study of feature extractors because of their importance in classification performance. The feature extraction is the most critical aspect of opinion classification since classification efficiency can be degraded if features are not properly chosen. A few scientific researchers have addressed the issue of feature extraction. And we found in the literature that almost every article deals with one or two feature extractors. For that, we decided in this paper to cover all the most popular feature extractors which are BOW, N-grams, TF-IDF, Word2vec, GloVe and FastText. In general, this paper will discuss the existing feature extractors in the opinion mining domain. Also, it will present the advantages and the inconveniences of each extractor. Moreover, a comparative study is performed for determining the most efficient combination CNN/extractor in terms of accuracy, precision, recall, and F1 measure.

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Survey of Sentiment Analysis Using Deep Learning Techniques

Cited by 47 publications

References 20 publications

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

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

COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

Evaluation of different extractors of features at the level of sentiment analysis

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