Large-Scale Sentiment Analysis for News and Blogs (system demonstration)

By Namrata Godbole, Manjunath Srinivasaiah, Steven S. Skiena. International Conference on Web and Social Media, 2007

684 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c6d0812caf2e429e97b659088f2b5f7288080ec7

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Citations in the corpus

Incorporating Structured Commonsense Knowledge in Story Completion (2018, background): “Recently, researchers studied large-scale sentiment analysis across news and blogs (Godbole, Srinivasaiah, and Skiena 2007), and also studied opinion mining on twitter (Patodkar and Sheikh 2010).”
Sentiment Analysis and Subjectivity (2010, background): “Sentiment classification is perhaps the most widely studied topic [3, 6, 8, 12, 13, 15, 16, 18, 27, 28, 33, 34, 35, 44, 45, 62, 64, 66, 67, 68, 70, 71, 73, 79, 80, 86, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 111].”
The impact of social and conventional media on firm equity value: A sentiment analysis approach (2013, background): “New research applying text-mining (TM) and natural language processing (NLP) techniques was developed to help people find business intelligence from free-formdata; however, thesemethods lack strength in detecting people's opinion [15].”
Predicting the Future with Social Media (2010, methodology): “Sentiment analysis is a well-studied problem in linguistics and machine learning, with different classiﬁers and language models employed in earlier work [13], [14].”
Author’s Sentiment Prediction (2020, background): “Sentiment analysis in news articles has been studied from various perspectives for different types of applications (Yu et al., 2013; Ahmad et al., 2007; Godbole et al., 2007; Balahur et al., 2013).”
Towards multimodal sentiment analysis: harvesting opinions from the web (2011, background): “Building upon these or other related resources, there is a growing body of work concerned with the automatic identification of sentiment in text, which often times addresses online text, such as reviews [18],news articles [3], or blogs [12].”
Opinion Mining and Sentiment Analysis (2008, background): “At least one opinion-tracking system rates subjectivity and sentiment separately [108].”
News impact on stock price return via sentiment analysis (2014, methodology): “In addition, Zhang and Skiena [44] proposed an approach based on sentiment polarity which is defined as Polarity ¼ positive negative positiveþ negative : ð8Þ We also implement this approach using sentiment polarity (PO) except changing from binary classification to three-category classification, whi”
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011, background): “…large related ﬁelds of opinion mining (Wilson et al., 2004; Popescu and Etzioni, 2005; Kim and Hovy, 2005; Missen and Boughanem, 2009) and sentiment analysis (Yi et al., 2003; Fei et al., 2006; Godbole, 2007; Missen and Boughanem, 2009) in text bear interesting alternatives and variants of methods.”
Forecasting: theory and practice (2020, background): “that denote the word’s positivity, negativity and neutrality based on existing lexical databases such as the WordNet (Godbole et al., 2007; Baccianella et al., 2010).”
Survey on mining subjective data on the web (2011, methodology): “Though, as was pointed out by Godbole et al. (2007), we should only consider paths that go through the words of the same polarity as initial.”
Meme-tracking and the dynamics of the news cycle (2009, background): “One could combine the approaches here with information about the political orientations of the different news media and blog sources [2, 12, 13], to see how particular threads move within and between opposed groups.”
Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information (2014, background): “Sentiment analysis is occasionally employed to analyze news at a semantic level [15,24,30,31].”