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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 Unsupervised Aspect Discovery from Online Consumer Reviews Suleman, Kaheer. - 2014 BASE Show details 2 Toward a unifying model for Opinion, Sentiment and Emotion information extraction Fraisse, Amel; Paroubek, Patrick In: The 9th International Conference on Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-01613403 ; The 9th International Conference on Language Resources and Evaluation, May 2014, Reykjavik, Iceland. pp.3881-3886 ; http://www.lrec-conf.org/proceedings/lrec2014/index.html (2014) BASE Show details 3 DCU: aspect-based polarity classification for SemEval task 4 Wagner, Joachim; Foster, Jennifer; Arora, Piyush... In: Wagner, Joachim orcid:0000-0002-8290-3849 , Arora, Piyush orcid:0000-0002-4261-2860 , Cortes, Santiago, Barman, Utsab, Bogdanova, Dasha, Foster, Jennifer orcid:0000-0002-7789-4853 and Tounsi, Lamia (2014) DCU: aspect-based polarity classification for SemEval task 4. In: International Workshop on Semantic Evaluation (SemEval-2014), 23-24 Aug 2014, Dublin, Ireland. ISBN 978-1-941643-24-2 (2014) BASE Show details 4 Annals of A Model to Compute Degree of Polarity of Review Titles Sohom Ghosh; Santanu Modak; Abhoy Ch In: http://www.researchmathsci.org/apamart/apam-v7n1-11.pdf (2014) Abstract: Abstract. Review Polarity Computation has been a flourishing frontier in the Natural Language Processing community. In this paper, we thoroughly study review titles of electronic products and compute the sentiment scores. Firstly, we conduct our experiment by collecting the review titles from a popular e-commerce website to build our dataset. Our dataset contains more than 1000 positive and negative review titles. For preprocessing, several NLP operations like tokenization, stop-word removal, stemming and so on have been done on the dataset. We build our own unique word corpora separately for positive and negative words. Finally, we design a new innovative model which automatically generates the scores by analyzing the review title. The score vary from-5 to +5. A score of-5 indicates that the review title is extremely negative and that of +5 indicates that it is highly affirmative. Experimental results confirm the high efficiency of our model. A product can be rated automatically as soon as a user writes the title of the review. Thus, the company can decide which reviews to display in their front page just by analyzing the title of the review. Keyword: linguistics; natural language processing; opinion mining; polarity; sentiment analysis URL: http://www.researchmathsci.org/apamart/apam-v7n1-11.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.682.6356 BASE Hide details 5 The USAGE review corpus for fine-grained, multi-lingual opinion analysis ... Klinger, Roman. - : Bielefeld University, 2014 BASE Show details 6 The USAGE review corpus for fine-grained, multi-lingual opinion analysis ... Klinger, Roman. - : Bielefeld University, 2014 BASE Show details 7 The USAGE review corpus for fine-grained, multi-lingual opinion analysis Klinger, Roman. - : Bielefeld University, 2014 BASE Show details 8 Three Essays on Opinion Mining of Social Media Texts Deng, Shuyuan In: Theses and Dissertations (2014) BASE Show details 9 Automatic creation of stock market lexicons for sentiment analysis using StockTwits data Oliveira, Nuno; Cortez, Paulo; Areal, Nelson. - : ACM, 2014 BASE Show details 10 Twitris v3: From Citizen Sensing to Analysis, Coordination and Action Purohit, Hemant; Sheth, Amit P. In: Amit P. Sheth (2014) BASE Show details

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