Estimating Reputation Polarity on Microblog Posts

Peetz, Maria-Hendrike; Rijke, Maarten de; Kaptein, Rianne

doi:10.1016/j.ipm.2015.07.003

Cited by 24 publications

(12 citation statements)

References 20 publications

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“…In their work, author had used urban dictionary in order to make clusters of all related words together and then applied kNN classification to these clusters so as to classify them into positive, negative and neutral clusters of high quality similar words sentiments. Peetz et al 53 had focused on the estimation of the polarity of the tweets in terms of reputation, for which DT was used for combining, learning and finding the optimal number of features in a set. In the preliminary experimentation, author had observed that the SVM and RF showed poor performances in comparison to DT for varied selected features.…”

Section: Literature Surveymentioning

confidence: 99%

“…The various benchmark datasets used in the past decade were WePS-3, 27 SemEval, 30,52,54,55,73,75,76,85 tweets prepared by Stanford University, 34,45,46,75 SNAP, 40 Sanders Twitter Sentiment Corpus (denoted as Sanders), 44,55,75,79 2008 Presidential Debate Corpus, 44,75,79 Sentiment140, 51 RepLab 2012, 53 RepLab 2013, 53 STS-manual, 55 Gold Standard personality labeled Twitter dataset, 59 Cleveland Heart Disease data, 69 STS-Gold, 73 FIGURE 6 Distribution of papers in accordance to the digital libraries (expressed in percentages) Many reported researches were carried on the tweets fetched directly from Twitter using its API. The tweets were from a variety of domains, topics and time period (referred as topic specific/topic oriented tweets).…”

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

confidence: 99%

“…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%

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

Kumar

Jaiswal

2019

Concurrency and Computation

130

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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...

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Section: Literature Surveymentioning

confidence: 99%

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

confidence: 99%

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

confidence: 99%

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

Kumar

Jaiswal

2019

Concurrency and Computation

130

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“…As an example, consider the entity "Japan" within the topic "#Whale hunting," with a negative sentiment. With the development of social media, we have witnessed a growth in the number of social media posts that express dynamically changing viewpoints in different languages around the same topic [38]. Unlike viewpoints in stationary documents, time-aware viewpoints of social text streams are dynamic, volatile and cross-linguistic [15].…”

Section: Introductionmentioning

confidence: 99%

“…The growth in the volume of social text streams motivates the development of methods that facilitate the understanding of those viewpoints. Their multi-lingual character is currently motivating an increasing volume of information retrieval research on multilingual social text streams, in areas as diverse as reputation polarity estimation [38] and entity-driven content exploration [43]. Recent work confirms that viewpoint summarization is an effective way of assisting users to understand viewpoints in stationary documents [17,19,26,29,30,34,46]-but viewpoint summarization for multilingual social text streams has not been addressed yet.…”

Section: Introductionmentioning

confidence: 99%

Time-aware Multi-Viewpoint Summarization of Multilingual Social Text Streams

Zhang

Inel²,

Aroyo³

et al. 2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. ABSTRACTA viewpoint is a triple consisting of an entity, a topic related to this entity and sentiment towards this topic. In time-aware multiviewpoint summarization one monitors viewpoints for a running topic and selects a small set of informative documents. In this paper, we focus on time-aware multi-viewpoint summarization of multilingual social text streams. Viewpoint drift, ambiguous entities and multilingual text make this a challenging task. Our approach includes three core ingredients: dynamic viewpoint modeling, cross-language viewpoint alignment, and, finally, multi-viewpoint summarization. Specifically, we propose a dynamic latent factor model to explicitly characterize a set of viewpoints through which entities, topics and sentiment labels during a time interval are derived jointly; we connect viewpoints in different languages by using an entity-based semantic similarity measure; and we employ an update viewpoint summarization strategy to generate a timeaware summary to reflect viewpoints. Experiments conducted on a real-world dataset demonstrate the effectiveness of our proposed method for time-aware multi-viewpoint summarization of multilingual social text streams.

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