Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Kumar, Akshi; Jaiswal, Arunima

doi:10.1002/cpe.5107

Cited by 133 publications

(64 citation statements)

References 80 publications

(208 reference statements)

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“…Opinion polarity takes into account the amount of positive or negative terms that appear in a given text (Kumar & Sebastian, 2012a;Kumar & Sebastian, 2012b;2012c, Kumar et al 2015Kumar & Joshi, 2017a;Kumar & Joshi, 2017b;Kumar & Jaiswal, 2017;Kumar & Khorwal, 2017;Kumar & Sharma, 2017;Son et al, 2019;Kumar & Jaiswal, 2019;Kumar & Garg, 2019). In this research, to determine the polarity of tweets, SentiWordNet is used.…”

Section: Negative Polarity Tweetsmentioning

confidence: 99%

Anxious Depression Prediction in Real-time Social Data

2019

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Anxiety Social mediaMachine learning A B S T R A C T Mental well-being and social media have been closely related domains of study. In this research a novel model, AD prediction model, for anxious depression prediction in real-time tweets is proposed. This mixed anxiety-depressive disorder is a predominantly associated with erratic thought process, restlessness and sleeplessness. Based on the linguistic cues and user posting patterns, the feature set is defined using a 5-tuple vector . An anxiety-related lexicon is built to detect the presence of anxiety indicators. Time and frequency of tweet is analyzed for irregularities and opinion polarity analytics is done to find inconsistencies in posting behaviour. The model is trained using three classifiers (multinomial naïve bayes, gradient boosting, and random forest) and majority voting using an ensemble voting classifier is done. Preliminary results are evaluated for tweets of sampled 100 users and the proposed model achieves a classification accuracy of 85.09%. . * https://twitter.com/msindiastudent

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Section: Negative Polarity Tweetsmentioning

confidence: 99%

Anxious Depression Prediction in Real-time Social Data

2019

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“…It has gradually emerged as a huge source of sentiment-rich data [4]. The keyword ''sentiment analysis'' was initially witnessed in the published work [5] in 2003, and since then, both primary [4], [6], [7] and secondary studies have been reported across pertinent literature [8]- [11]. Furthermore, the literature is well-equipped with studies pertaining to sentiment analysis using machine learning paradigms on specifically textual user-generated online content on social media.…”

Section: Related Workmentioning

confidence: 99%

“…The output is thus a weighted combination of all the input states, not just the last state. The attention mechanism gives an attention score e i,t to each word t in the sentence i, as given by (11).…”

Section: ) Attention Layermentioning

confidence: 99%

Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network

et al. 2019

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A large community of research has been developed in recent years to analyze social media and social networks, with the aim of understanding, discovering insights, and exploiting the available information. The focus has shifted from conventional polarity classification to contemporary applicationoriented fine-grained aspects such as, emotions, sarcasm, stance, rumor, and hate speech detection in the user-generated content. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we propose a deep learning model called sAtt-BLSTM convNet that is based on the hybrid of soft attention-based bidirectional long shortterm memory (sAtt-BLSTM) and convolution neural network (convNet) applying global vectors for word representation (GLoVe) for building semantic word embeddings. In addition to the feature maps generated by the sAtt-BLSTM, punctuation-based auxiliary features are also merged into the convNet. The robustness of the proposed model is investigated using balanced (tweets from benchmark SemEval 2015 Task 11) and unbalanced (approximately 40000 random tweets using the Sarcasm Detector tool with 15000 sarcastic and 25000 non-sarcastic messages) datasets. An experimental study using the training-and test-set accuracy metrics is performed to compare the proposed deep neural model with convNet, LSTM, and bidirectional LSTM with/without attention and it is observed that the novel sAtt-BLSTM convNet model outperforms others with a superior sarcasm-classification accuracy of 97.87% for the Twitter dataset and 93.71% for the random-tweet dataset. INDEX TERMS Sarcasm, deep learning, attention, social data.

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“…Kumar and Jaiswal presented a systematic literature review to collate, explore, understand, and analyze the efforts and trends in a well‐structured manner to identify research gaps defining the future prospects of this coupling. The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter, and secondly, as compared to the previous reviews, we adopt a systematic approach to identify and gather empirical evidence, interpret results, and critically analyze and integrate the findings of all relevant high‐quality studies to address specific research questions pertaining to the defined research domain.…”

Section: Theme Of This Special Issuementioning

confidence: 99%