Context-Based Sentiment Analysis: A Survey

Ansari, Oumayma El; Zahir, Jihad; Mousannif, Hajar

doi:10.1007/978-3-030-02852-7_8

Cited by 7 publications

(8 citation statements)

References 8 publications

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“…Another problem with traditional sentiment analysis models is the lack of context in labeling lexical items and extracting features, which results in ambiguity in polarity representations. A word or group of words can have different meanings and polarities in different contexts; hence, the global representation of words and sentences could influence the semantics of the words [43]. More contemporary developments in sentiment analysis are shifting toward increasingly context-based sentiment analysis and applicability in other languages [43][44][45].…”

Section: Sentiment Analysismentioning

confidence: 99%

“…A word or group of words can have different meanings and polarities in different contexts; hence, the global representation of words and sentences could influence the semantics of the words [43]. More contemporary developments in sentiment analysis are shifting toward increasingly context-based sentiment analysis and applicability in other languages [43][44][45]. Context-aware (ie, context-based) sentiment analysis attempts to tackle the challenge of ambiguity by taking into account all of the text around any given word or words, then processing the logical structure of the sentences, establishing the relations between semantic concepts and assigning logical grammatical roles to the lexical elements to decipher the most relevant meaning of a word or group of words that have more than one definition.…”

Section: Sentiment Analysismentioning

confidence: 99%

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Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study (Preprint)

Gbashi¹,

Adebo²,

Doorsamy³

et al. 2020

Preprint

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BACKGROUND The global onset of the Coronavirus disease 2019 (COVID-19) has resulted in significant public health and socio-economic impacts. The necessity for an immediate medical breakthrough is unprecedentedly compelling. However, parallel to the emergence of the COVID-19 pandemic is the proliferation of information regarding the pandemic, which, if uncontrolled, can not only mislead the public, but also hinder the concerted efforts of relevant stakeholders in mitigating against the effect of this pandemic. It is known that media communications can significantly affect public perception and attitude towards a medical treatment, vaccinatiion or a subject matter, particularly when the population has limited knowledge on the subject. OBJECTIVE The presented study attempts to systematically scrutinize media communications (google news headlines/snippets and twitter posts) in order to understand prevailing sentiments regarding COVID-19 vaccines in Africa. METHODS This was achieved using three standard computational linguistics models - i.e., TextBlob, VADER, and Word2Vec-BiLSTM. RESULTS A total of 637 twitter posts and 569 google news headlines/descriptions retrieved between February 02, 2020 to May 05, 2020 were analyzed. Interestingly, the results revealed that contrary to general perceptions, google news headlines/snippets and twitter posts within the stated period were generally passive to positive towards COVID-19 vaccines in Africa. It was possible to understand these patterns in light of increasingly sustained efforts by various media and health actors in ensuring availability of healthy and factual information about the pandemic. CONCLUSIONS This type of analysis could contribute to understanding predominant polarities and associated potential attitudinal inclinations. Such knowledge could be critical in informing relevant public health and media engagement policies.

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Section: Sentiment Analysismentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study (Preprint)

Gbashi¹,

Adebo²,

Doorsamy³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Generally, there are two approaches 4 : Frame-to-frame algorithm (FFA) and frame-to-reference algorithm (FRA). DIS can be used along with MIS to have high degree of jitter attenuation 8,14,15 .…”

Section: Digital Image Stabilisationmentioning

confidence: 99%

“…A typical testing setup 27 for lab evaluation is given in Factory calibration range, accuracy and retention are also validated. Sights are to be evaluated for EMI/EMC and environmental conditions too.…”

Section: Lab Evaluationmentioning

confidence: 99%

Trends in Sighting Systems for Combat Vehicles

et al. 2017

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Search and tracking in dynamic condition, rapid re-targeting, precision pointing and long range engagement in day and night condition are core requisite of stabilised sighting systems used for combat vehicles. Complex battle field requires integrated fire control system with stabilised sighting system as its main constituent. It facilitates quick reaction to fire control system and provides vital edge in the battlefield scenario. Precision gimbal design, optics design, embedded engineering, control system, electro-optical sensors, target detection and tracking, panorama generation, auto-alerting, digital image stabilisation, image fusion and integration are important aspects of sighting system development. In this paper, design considerations for a state of art stabilised sighting system have been presented including laboratory and field evaluation methods for such systems.

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“…Another problem with traditional sentiment analysis models is the lack of context in labeling lexical items and extracting features, which results in ambiguity in polarity representations. A word or group of words can have different meanings and polarities in different contexts; hence, the global representation of words and sentences could influence the semantics of the words [ 43 ]. More contemporary developments in sentiment analysis are shifting toward increasingly context-based sentiment analysis and applicability in other languages [ 43 - 45 ].…”

Section: Introductionmentioning

confidence: 99%

Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study

et al. 2021

View full text Add to dashboard Cite

Background The global onset of COVID-19 has resulted in substantial public health and socioeconomic impacts. An immediate medical breakthrough is needed. However, parallel to the emergence of the COVID-19 pandemic is the proliferation of information regarding the pandemic, which, if uncontrolled, cannot only mislead the public but also hinder the concerted efforts of relevant stakeholders in mitigating the effect of this pandemic. It is known that media communications can affect public perception and attitude toward medical treatment, vaccination, or subject matter, particularly when the population has limited knowledge on the subject. Objective This study attempts to systematically scrutinize media communications (Google News headlines or snippets and Twitter posts) to understand the prevailing sentiments regarding COVID-19 vaccines in Africa. Methods A total of 637 Twitter posts and 569 Google News headlines or descriptions, retrieved between February 2 and May 5, 2020, were analyzed using three standard computational linguistics models (ie, TextBlob, Valence Aware Dictionary and Sentiment Reasoner, and Word2Vec combined with a bidirectional long short-term memory neural network). Results Our findings revealed that, contrary to general perceptions, Google News headlines or snippets and Twitter posts within the stated period were generally passive or positive toward COVID-19 vaccines in Africa. It was possible to understand these patterns in light of increasingly sustained efforts by various media and health actors in ensuring the availability of factual information about the pandemic. Conclusions This type of analysis could contribute to understanding predominant polarities and associated potential attitudinal inclinations. Such knowledge could be critical in informing relevant public health and media engagement policies.

show abstract

Context-Based Sentiment Analysis: A Survey

Cited by 7 publications

References 8 publications

Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study (Preprint)

Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study (Preprint)

Trends in Sighting Systems for Combat Vehicles

Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study

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