Sentiment analysis: a review and comparative analysis over social media

Singh, Nikhil Kumar; Tomar, Deepak Singh; Sangaiah, Arun Kumar

doi:10.1007/s12652-018-0862-8

Cited by 73 publications

(35 citation statements)

References 66 publications

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“…Although many sentiment analysis tools based on natural language processing or machine learning [ 22 , 23 ] are able to automatically extract emotions from the text by utilizing less time and labor, the accuracy of machine identification is lower than that of manual identification. Some simple classifications, such as positive or negative, can be detected through machine identification; however, for more subtle discrete emotions, such as anger, fear, and happiness, manual identification would yield better accuracy.…”

Section: Methodsmentioning

confidence: 99%

Public Emotions and Rumors Spread During the COVID-19 Epidemic in China: Web-Based Correlation Study

Dong¹,

Tao²,

Xia³

et al. 2020

J Med Internet Res

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Background Various online rumors have led to inappropriate behaviors among the public in response to the COVID-19 epidemic in China. These rumors adversely affect people’s physical and mental health. Therefore, a better understanding of the relationship between public emotions and rumors during the epidemic may help generate useful strategies for guiding public emotions and dispelling rumors. Objective This study aimed to explore whether public emotions are related to the dissemination of online rumors in the context of COVID-19. Methods We used the web-crawling tool Scrapy to gather data published by People’s Daily on Sina Weibo, a popular social media platform in China, after January 8, 2020. Netizens’ comments under each Weibo post were collected. Nearly 1 million comments thus collected were divided into 5 categories: happiness, sadness, anger, fear, and neutral, based on the underlying emotional information identified and extracted from the comments by using a manual identification process. Data on rumors spread online were collected through Tencent’s Jiaozhen platform. Time-lagged cross-correlation analyses were performed to examine the relationship between public emotions and rumors. Results Our results indicated that the angrier the public felt, the more rumors there would likely be (r=0.48, P<.001). Similar results were observed for the relationship between fear and rumors (r=0.51, P<.001) and between sadness and rumors (r=0.47, P<.001). Furthermore, we found a positive correlation between happiness and rumors, with happiness lagging the emergence of rumors by 1 day (r=0.56, P<.001). In addition, our data showed a significant positive correlation between fear and fearful rumors (r=0.34, P=.02). Conclusions Our findings confirm that public emotions are related to the rumors spread online in the context of COVID-19 in China. Moreover, these findings provide several suggestions, such as the use of web-based monitoring methods, for relevant authorities and policy makers to guide public emotions and behavior during this public health emergency.

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Section: Methodsmentioning

confidence: 99%

Public Emotions and Rumors Spread During the COVID-19 Epidemic in China: Web-Based Correlation Study

Dong¹,

Tao²,

Xia³

et al. 2020

J Med Internet Res

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show abstract

“…After examine the text feature extraction (POS, BOW, HT) and scope of negation (CWA, PMI, GDT) technique, proposed framework present nine one too many feature fusion case from Text feature to Scope of negation as shown in table. Support vector machine for sentiment classification [3], classifier the preprocessed massage dataset M f f after feature fusion. Where the performance of polarity classification depend upon type of feature fusion applied.…”

Section: Sentiment Classificationmentioning

confidence: 99%

“…2) Naive Bayes: In proposed framework Naïve Bayes determine the polarity class (+ve,-ve) of any preprocessed massage data set M f f after feature fusion on the basis of maximum posterior probability as shown in equation 11 and 12 [3].…”

Section: Sentiment Classificationmentioning

confidence: 99%

“…In current time Sentiment Analysis, subjectivity and Opinion mining enthralled significant interest from both the research community and Marketing Agency [2]. The main purpose of sentiment analysis is to rank the opinion according to its level of positive, negative or neutral polarity [3]. Sentiment analysis have many applications, ranging from product analysis [4]to improving sales and marketing strategies, predicting stock market fluctuations [5], identifying changes ideological in political issues [6] , in the prediction of film critics [7] and in Electronic Government.…”

Section: Introductionmentioning

confidence: 99%

“…idioms, SA sarcastic sentences and handle negation and identify polarity. mark in negative feelings [3]. Here in this paper we have summarized the effect of negation cues over sentiment analysis and introduced a comparative analysis of recent text feature extraction, negation cues and scope detection technique.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature Fusion for Negation Scope Detection in Sentiment Analysis: Comprehensive Analysis over Social Media

Singh¹,

Singh²

2019

IJACSA

Self Cite

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Negation control for sentiment analysis is essential and effective decision support system. Negation control include identification of negation cues, scope of negation and their influence within it. Negation can either shift or change the polarity score of opinionated word. This paper present a framework for feature fusion of text feature extraction, negation cue and scope detection technique for enhancing the performance of recent sentiment classifier for negation control. Explore text feature POS, BOW and HT with negation cue and scope detection techniques for classification technique over social media data set. This paper has included the evaluation of sentiment classification (Support vector machine, Navies Bayes, Linear Regression and Random Forest) and Nine feature fusion over presented prepossessing framework. This paper yield interesting result about collective response of feature fusion for negation scope detection and classification technique. Feature Fusion vector significantly increase the polarity classification accuracy of sentiment classification technique. POS with Grammatical dependency tree can detect negation with better accuracy as compared to other feature fusion.

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A Novel Deep Belief Neural Framework Based on Ant Colony to Analyze the Sentiment Score in Drug Review Dataset

Alsaduni,

Baseer,

Alluhaidan

et al. 2024

Lecture Notes in Networks and Systems

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Sentiment analysis: a review and comparative analysis over social media

Cited by 73 publications

References 66 publications

Public Emotions and Rumors Spread During the COVID-19 Epidemic in China: Web-Based Correlation Study

Public Emotions and Rumors Spread During the COVID-19 Epidemic in China: Web-Based Correlation Study

Feature Fusion for Negation Scope Detection in Sentiment Analysis: Comprehensive Analysis over Social Media

A Novel Deep Belief Neural Framework Based on Ant Colony to Analyze the Sentiment Score in Drug Review Dataset

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