An efficient framework for real-time tweet classification

Khan, Imran; Naqvi, S. Kazim; Alam, Mansaf; Rizvi, Sam

doi:10.1007/s41870-017-0015-x

Cited by 19 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Social Network analysis The growing pace of internet with world connectivity made social connects an ease. The networking skills sorts many concerns from identifying talent to pitch for business [ 40 ], look for training association to comparing performances of various software and many more. In all builds interrelationships in various fields and business front.…”

Section: Results and Analysismentioning

confidence: 99%

Fearless path for human resource personnel’s through analytics: a study of recent tools and techniques of human resource analytics and its implication

Saxena

Bagga

Gupta

2021

Int. j. inf. tecnol.

View full text Add to dashboard Cite

Section: Results and Analysismentioning

confidence: 99%

Fearless path for human resource personnel’s through analytics: a study of recent tools and techniques of human resource analytics and its implication

Saxena

Bagga

Gupta

2021

Int. j. inf. tecnol.

View full text Add to dashboard Cite

“…Since the performance of Naive Bayes classifier is low, so we have experimented our model on another classifier proposed in [35]. For this classifier we have used three different word lists (for politics, sports and entertainment) each having 200 words initially and perform iterations to classify tweets.…”

Section: Methodsmentioning

confidence: 99%

An Efficient Framework for Creating Twitter Mart on a Hybrid Cloud

Khan¹,

Naqvi²,

Alam³

et al. 2017

IJITCS

Self Cite

View full text Add to dashboard Cite

Abstract-The contemporary era of technological quest is buzzing with two words -Big Data and Cloud Computing. Digital data is growing rapidly from Gigabytes (GBs), terabytes (TBs) to Petabytes (PBs), and thereby burgeoning data management challenges. Social networking sites like Twitter, Facebook, Google+ etc generate huge data chunks on daily basis. Among them, twitter masks as the largest source of publicly available mammoth data chunks intended for various objectives of research and development. In order to further research in this fast emerging area of managing Big Data, we propose a novel framework for doing analysis on Big Data and show its implementation by creating a "Twitter Mart" which is a compilation of subject specific tweets that address some of the challenges for industries engaged in analyzing subject specific data. In this paper, we adduce algorithms and an holistic model that aids in effective stockpiling and retrieving data in an efficient manner.

show abstract

“…Apple; Google; Microsoft, 27,29,34,44,55,73,75 consumer products like kindle, smart-phones, etc, 51,55,56,73,84 natural calamities, energy and environmental related, 36,46,77 cyber hatred, 47,62 entertainment 29,49,53,84 which includes tweets about music and movies, automotive or vehicles, 49,53 banking, 53 government or public campaign or public administration, 33,37,39,57,72,79 education or universities, 43,53,57,67 science or technology, 40,49,57 politics, 17,39,40,49,61,67,72,79,80,84 sports, 40,49,60 daily deals and discount, 56 trade or commercial services or business or financial...…”

Section: • Widely Used Datasets and Domains In Which The Studies For mentioning

confidence: 99%

“…A higher precision27-29,31-33,35,37-39,43-45,47,49-51,56-62, value indicates fewer ''false positives'' (FP) and vice versa. It is64,66,68,70,74,75,80,81,83,84…”

mentioning

confidence: 99%

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

Kumar

Jaiswal

2019

Concurrency and Computation

131

View full text Add to dashboard Cite

Sentiment detection and classification is the latest fad for social analytics on Web. With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non-trivial. With a marked increase in the size, subjectivity, and diversity of social web-data, the vagueness, uncertainty and imprecision within the information has increased manifold. Soft computing techniques have been used to handle this fuzziness in practical applications. This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter. We present 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, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high-quality studies to address specific research questions pertaining to the defined research domain. KEYWORDS machine learning, review, sentiment analysis, soft computing, Twitter INTRODUCTIONThe incessantly evolving dynamics of the Web in terms of the volume, velocity and variety of opinion-rich information accessible online, has made research in the domain of Sentiment Analysis (SA) a trend for many practical applications which facilitate decision support and deliver targeted information to domain analysts. Interestingly, the buzzing term ''big data'' which is estimated to be 90% unstructured 1 further makes it crucial to tap and analyze information using contemporary tools. Text mining models define the process to transform and substitute this unstructured data into a structured one for knowledge discovery. Use of classification algorithms to intelligently mine text has been studied extensively across literature. 2,3 SA, established as a typical text classification task, 4 is defined as the computational study of people's opinions, attitudes and emotions towards an entity. 5,6 It offers a technology-based solution to understand people's reactions, views and opinion polarities (positive, negative or neutral) in textual content available over social media sources.Research studies and practical applications in the field of SA have escalated in the past decade with the transformation and expansion of Web from passive provider of content to an active socially-aware distributor of collective intelligence. This new collaborative Web (called Web 2.0), 7 extended by Web-based technologies like comments, blogs and wikis, social media portals like Twitter or Facebook, that allow to build social networks based on professional relationship, i...

show abstract

An efficient framework for real-time tweet classification

Cited by 19 publications

References 9 publications

Fearless path for human resource personnel’s through analytics: a study of recent tools and techniques of human resource analytics and its implication

Fearless path for human resource personnel’s through analytics: a study of recent tools and techniques of human resource analytics and its implication

An Efficient Framework for Creating Twitter Mart on a Hybrid Cloud

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

Contact Info

Product

Resources

About