Creating sentiment dictionaries via triangulation

Shinar, J.; Lenkova, Polina; Ebrahim, Mohamed; Ehrman, Maud; Hürriyetoğlu, Ali; Kabadjov, Mijail; Steinberger, Ralf; Tanev, Hristo; Zavarella, Vanni; Vázquez, Silvia Rodríguez

doi:10.1016/j.dss.2012.05.029

Cited by 87 publications

(41 citation statements)

References 8 publications

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“…Based on this promise, various researchers have focused on developing and improving sentiment dictionaries (Balahur et al, 2011;Maks & Vossen, 2012;Steinberger et al, 2012;Tufiş & Ştefănescu, 2012). This research has also strived to go beyond the typical sentiment-detection problems to emphasize multilingual sentiment analysis (Steinberger et al, 2012), actor subjectivity (Maks & Vossen, 2012), and irony detection (Reyes & Rosso, 2012). Recent research has also focused on going beyond supervised lexicon development to semi-automatic and automatic machine learning approaches (Bai, 2011;Duric & Song, 2012).…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Hatzivassiloglou and McKeown (1997) report that fully automated sentiment tools can identify sentiment words and their respective polarity in sentences with an accuracy level (compared to human experts) as high as 82 percent. Based on this promise, various researchers have focused on developing and improving sentiment dictionaries (Balahur et al, 2011;Maks & Vossen, 2012;Steinberger et al, 2012;Tufiş & Ştefănescu, 2012). This research has also strived to go beyond the typical sentiment-detection problems to emphasize multilingual sentiment analysis (Steinberger et al, 2012), actor subjectivity (Maks & Vossen, 2012), and irony detection (Reyes & Rosso, 2012).…”

Section: Sentiment Analysismentioning

confidence: 99%

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The Impact of Sentiment Analysis Output on Decision Outcomes: An Empirical Evaluation

Lak¹,

Turetken²

2017

THCI

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Abstract:User-generated online content serves as a source of product-and service-related information that reduces the uncertainty in consumer decision making, yet the abundance of such content makes it prohibitively costly to use all relevant information. Dealing with this (big data) problem requires a consumer to decide what subset of information to focus on. Peer-generated star ratings are excellent tools for one to decide what subset of information to focus on as they indicate a review's "tone". However, star ratings are not available for all user-generated content and not detailed enough in other cases. Sentiment analysis, a text-analytic technique that automatically detects the polarity of text, provides sentiment scores that are comparable to, and potentially more refined than, star ratings. Despite its popularity as an active topic in analytics research, sentiment analysis outcomes have not been evaluated through rigorous user studies. We fill that gap by investigating the impact of sentiment scores on purchase decisions through a controlled experiment using 100 participants. The results suggest that, consistent with the effort-accuracy trade off and effort-minimization concepts, sentiment scores on review documents improve the efficiency (speed) of purchase decisions without significantly affecting decision effectiveness (confidence).

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Section: Sentiment Analysismentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

The Impact of Sentiment Analysis Output on Decision Outcomes: An Empirical Evaluation

Lak¹,

Turetken²

2017

THCI

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“…9) New Avenues in OM and Sentiment Analysis, which have valuable, vast, and unstructured information about public opinion was proposed by Cambria, et al, [12] where history, current use, and future of OM and sentiment analysis were discussed, with techniques and tools. 10) Steinberger et al [13] suggested a semi-automatic method for creating dictionaries of opinion in different languages and launched high-level opinion dictionaries for two languages and automatic translation into a third language. The words discovered in the word list of the target language are normally used similarly to the meaning of the word in the two source languages.…”

Section: )mentioning

confidence: 99%

Current Scenario of Opinion Mining & Sentimental Analysis

Kushwah¹

2018

IJRASET

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Nowadays, social networking and online shopping sites are being used by the public to express their opinion about a particular entity. Entity may be any product, event or people etc. Corporate houses, government organizations and other rating agencies are always eager to analyze these opinions/views, which help them in taking strategic decisions. The analysis of opinions for some useful, hidden and previously unknown information is called Opinion Mining. Sentiment Analysis is the process of detecting the emotional reaction from the collection of opinion/ review. The goal of Opinion Mining and Sentiment Analysis is to make computer more intelligent to recognize and express emotions of user's. Currently some opinion mining and sentiment analysis techniques are available in the literature that processes the review, which are gathered from different user's view. However, these techniques are unable to remove noise from the data (review), discovering of sentiment and their polarity in complex sentences and extraction of multiple opinions from the same document etc. This paper describes a present scenario of available techniques and algorithms that are being used currently in opinion mining and sentiment analysis. Authors have also tried to put some light on some challenges and other related issues relevant to these techniques and algorithms.

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“…Because the largest part of the state of the art lexicons focuses on the English language, Italian lexical databases are mostly created by translating and adapting the English ones (Steinberger et al, 2012;Baldoni et al, 2012;Hernandez-Farias et al, 2014).…”

Section: State Of the Artmentioning

confidence: 99%

Proceedings of the Second Italian Conference on Computational Linguistics CLiC-it 2015

Bosco¹,

Tonelli²,

Zanzotto³

2015

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CLiC-it 2015 is held in Trento on December 3-4 2015, hosted and locally organized by Fondazione Bruno Kessler (FBK), one the most important Italian research centers for what concerns CL. The organization of the conference is the result of a fruitful conjoint effort of different research groups (Università di Torino, Università di Roma Tor Vergata and FBK) showing the nationwide spreading of CL in Italy. As in the first edition, the main aim of the event is at establishing a reference forum on CL, covering all the aspects needed to describe the multi-faceted and cross-disciplinary reality of the involved research topics and of the Italian community working in this area. Indeed the spirit of CLiC-it is inclusive, in order to build a scenario as much as possible comprehensive of the complexity of language phenomena and approaches to address them, bringing together researchers and scholars with different competences and skills and working on different aspects according to different perspectives. The large number of researchers that have decided to present their work at CLiC-it and the number of directions here investigated are proof of the maturity of our community and a promising indication of its vitality. We received a total of 64 paper submissions, out of which 52 have been accepted to appear in the Conference Proceedings, which are available online and on the OpenEdition platform. Overall, we collected 129 authors from 15 countries.

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Creating sentiment dictionaries via triangulation

Abstract: * endorsed by SIGNLL-ACL's Special Interest Group on Natural Language Learning * endorsed by SIGANN-ACL's Special Interest Group for Annotation * Extended versions of the best papers will be chosen for a special issue of the Decision Support Systems journal (published by Elsevier).

Cited by 87 publications

References 8 publications

The Impact of Sentiment Analysis Output on Decision Outcomes: An Empirical Evaluation

The Impact of Sentiment Analysis Output on Decision Outcomes: An Empirical Evaluation

Current Scenario of Opinion Mining & Sentimental Analysis

Proceedings of the Second Italian Conference on Computational Linguistics CLiC-it 2015

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