Towards the Exploitation of Statistical Language Models for Sentiment Analysis of Twitter Posts

Bhattacharya, Swapan; Banerjee, Prasun

doi:10.1007/978-3-319-59105-6_22

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…A total of 6 studies [307,242,78,308,177, 81] adopted a probabilistic approach to perform a form of social opinion mining. In particular, [78] propose a novel probabilistic model in the Content and Link Unsupervised Sentiment Model (CLUSM), where the focus is on microblog sentiment classification incorporating link information, namely behaviour, same user and friend.…”

Section: Probabilistic (Pr)mentioning

confidence: 99%

Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

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Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing and other

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Section: Probabilistic (Pr)mentioning

confidence: 99%

Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

View full text Add to dashboard Cite

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Development of argument based opinion mining model with sentimental data analysis from twitter content

Arumugam

2022

Concurrency and Computation

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In present scenario, social networks have developed massive in practice and society impact. Specifically, micro-blogging is on trend in various platforms, such as Twitter, Instagram to evaluate public opinions for various issues. In recent times, some methods are developed for evaluating Twitter messages, based on the sentiment and opinions presented in tweets, corresponding to the hash-tags and keywords. However, these models have some issues in handling the contradictory content and inconsistent data.Considering with this, this article presents an argument based opinion mining model with sentimental data analysis, for extracting specific argument, which is assessed in bottom-up manner from the content from society emotion's reflects on the messages. Moreover, this model makes the user to pull out the arguments from a document set, which contains content from commercial sites, to extract the mostly argued positive and negative content. This model use natural language processing techniques, extraction of argument words for defining the decisions. The classification Naive Bayes classification is used for categorizing the results widely under agreed or disagreed. The experimental results prove that the proposed model provides feasible and appropriate results in argument analysis from Twitter content.

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Over a decade of social opinion mining: a systematic review

Cortis

Davis

2021

Artif Intell Rev

View full text Add to dashboard Cite

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.

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Towards the Exploitation of Statistical Language Models for Sentiment Analysis of Twitter Posts

Cited by 4 publications

References 21 publications

Over a Decade of Social Opinion Mining: A Systematic Review

Over a Decade of Social Opinion Mining: A Systematic Review

Development of argument based opinion mining model with sentimental data analysis from twitter content

Over a decade of social opinion mining: a systematic review

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