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1	Generating linked-data based domain-specific sentiment lexicons from legacy language and semantic resources
	Vulcu, Gabriela; Buitelaar, Paul; Pereira, Bianca. - : European Language Resources Association, 2019
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2	An exploration of a financial lexicon-based approach to sentiment analysis and its application to financial news and reports
	Kirchner, Avery N., 1997--. - : Northern Illinois University, 2019
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3	Detecting and Monitoring Hate Speech in Twitter
	Juan Carlos Pereira-Kohatsu; Lara Quijano-Sánchez; Federico Liberatore...
	In: Sensors ; Volume 19 ; Issue 21 (2019)
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4	JÄMFÖRELSE AV ATTITYDANALYS ALGORITMER FÖR SPELOMDÖMEN ; COMPARISON OF SENTIMENT ANALYSIS ALGORITHMS FOR GAME REVIEWS
	Gernandt, Niclas; Farhod, Jaser. - : KTH, Hälsoinformatik och logistik, 2019
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5	Computing the Affective-Aesthetic Potential of Literary Texts
	Arthur M. Jacobs; Annette Kinder
	In: AI ; Volume 1 ; Issue 1 ; Pages 2-27 (2019)
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6	Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches
	Jurgita Kapočiūtė-Dzikienė; Robertas Damaševičius; Marcin Woźniak
	In: Computers ; Volume 8 ; Issue 1 (2019)
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7	Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
	Chakriswaran; Vincent; Srinivasan...
	In: Applied Sciences ; Volume 9 ; Issue 24 (2019)
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8	Semantic Features for Optimizing Supervised Approach of Sentiment Analysis on Product Reviews
	Bagus Setya Rintyarna; Riyanarto Sarno; Chastine Fatichah
	In: Computers ; Volume 8 ; Issue 3 (2019)
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9	Incorporating Background Checks with Sentiment Analysis to Identify Violence Risky Chinese Microblogs
	Yun-Fei Jia; Shan Li; Renbiao Wu
	In: Future Internet ; Volume 11 ; Issue 9 (2019)
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10	Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions
	Sattam Almatarneh; Pablo Gamallo
	In: Information ; Volume 10 ; Issue 1 (2019)
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11	Sentiment Analysis for Social Media
	Carlos A. Iglesias; Antonio Moreno
	In: Applied Sciences ; Volume 9 ; Issue 23 (2019)
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12	#Globalcitizen: An Explorative Twitter Analysis of Global Identity and Sustainability Communication
	Marie Merle; Gerhard Reese; Stefan Drews
	In: Sustainability ; Volume 11 ; Issue 12 (2019)
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13	Fake News and Propaganda: Trump’s Democratic America and Hitler’s National Socialist (Nazi) Germany
	David E. Allen; Michael McAleer
	In: Sustainability ; Volume 11 ; Issue 19 (2019)
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14	SocialTERM-Extractor: Identifying and Predicting Social-Problem-Specific Key Noun Terms from a Large Number of Online News Articles Using Text Mining and Machine Learning Techniques
	Suh
	In: Sustainability ; Volume 11 ; Issue 1 (2019)
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15	A CNN-BiLSTM Model for Document-Level Sentiment Analysis
	Maryem Rhanoui; Mounia Mikram; Siham Yousfi...
	In: Machine Learning and Knowledge Extraction ; Volume 1 ; Issue 3 ; Pages 48-847 (2019)
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16	Sentiment Analysis of Twitter Data (saotd) ...
	Munson, Evan; Smith, Christopher; Boehmke, Bradley. - : Zenodo, 2019
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17	Sentiment Analysis of Twitter Data (saotd) ...
	Munson, Evan; Smith, Christopher; Boehmke, Bradley. - : Zenodo, 2019
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18	Text mining with word embedding for outlier and sentiment analysis
	Zhuang, Honglei. - 2019
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19	Semiautomatic dictionary-based tweet classification for measuring well-being
	Cameletti, M. (orcid:0000-0002-6502-7779); Fabris, S.; Schlosser, S.; Toninelli, D. (orcid:0000-0002-3158-1982). - : Università degli studi di Bergamo, 2019. : country:IT, 2019. : place:Bergamo, 2019
	Abstract: In this paper we describe a semiautomatic dictionary-based approach to filter tweets talking about specific topics. In particular, we are interested in studying the citizen well-being (WB) and, for this aim, we select tweets pertaining two WB dimensions such as environment and health. For this purpose, we use dictionaries containing keywords selected by analyzing tweets published by some Official Social Accounts linked with the two topics. The selected tweets are then processed in order to estimate the sentiment of the population with respect to such specific subjects. In this paper, we present some preliminary results for Great Britain (GB) using tweet collected on the whole country for the six-weeks period from 2019/01/14 to 2019/02/24. The results show that, on the one hand, our dictionary-based classification approach reaches good levels of accuracy, sensitivity and specificity; on the other hand, we assess the spatial variability across GB of the two dimensions we are studying by means of the tweets sentiment analysis.
	Keyword: environment; health; sentiment analysis; Settore SECS-S/01 - Statistica; spatial analysis; Twitter
	URL: http://hdl.handle.net/10446/146844 https://doi.org/10.6092/GRASPA19_pp66-69
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20	Weakly supervised sentiment analysis and opinion extraction
	Angelidis, Stefanos. - : The University of Edinburgh, 2019
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